Method for identifying biomarkers using a probability map

ABSTRACT

A method of forming a probability map is disclosed. According to one embodiment, a method may include: (1) obtaining multiple measures of multiple imaging parameters for every stop of a moving window on an image, wherein two neighboring ones of the stops of the moving window are partially overlapped with each other; (2) obtaining first probabilities of an event for the stops of the moving window by matching the measures of the imaging parameters to a classifier; and (3) obtaining second probabilities of the event for multiple voxels of a probability map based on information associated with the first probabilities.

BACKGROUND OF THE DISCLOSURE

This application is a continuation of U.S. application Ser. No.14/821,703, now U.S. Pat. No. 9,922,433, filed Aug. 8, 2015, whichclaims priority to U.S. application Ser. No. 62/167,940, filed on May29, 2015, the contents of which are incorporated herein by reference intheir entireties.

FIELD OF THE DISCLOSURE

The disclosure relates to a method of forming a probability map, andmore particularly, to a method of forming a probability map based onmolecular and structural imaging data, such as magnetic resonanceimaging (MRI) parameters, computed tomography (CT) parameters, positronemission tomography (PET) parameters, single-photon emission computedtomography (SPECT) parameters, micro-PET parameters, micro-SPECTparameters, Raman parameters, and/or bioluminescence optical (BLO)parameters, or based on other structural imaging data, such as from CTand/or ultrasound images.

BRIEF DESCRIPTION OF THE RELATED ART

Big Data represents the information assets characterized by such a highvolume, velocity and variety to require specific technology andanalytical methods for its transformation into value. Big Data is usedto describe a wide range of concepts: from the technological ability tostore, aggregate, and process data, to the cultural shift that ispervasively invading business and society, both drowning in informationoverload. Precision medicine is a medical model that proposes thecustomization of healthcare—with medical decisions, practices, and/orproducts being tailored to the individual patient. In this model,diagnostic testing is often employed for selecting appropriate andoptimal therapies based on the context of a patient's genetic content orother molecular or cellular analysis.

SUMMARY OF THE DISCLOSURE

The invention proposes an objective to provide a method of forming aprobability map based on molecular and/or structural imaging data, suchas MRI parameters, CT parameters, PET parameters, SPECT parameters,micro-PET parameters, micro-SPECT parameters, Raman parameters, and/orBLO parameters, and/or other structural imaging data, such as from CTand/or ultrasound images, for a first subject (e.g., an individualpatient). The method may build a dataset or database of big datacontaining molecular and/or structural imaging data (and/or otherstructural imaging data) for multiple second subjects and biopsytissue-based data associated with the molecular and/or structuralimaging data for the second subjects. A classifier or biomarker librarymay be constructed or established from the dataset or database of bigdata. The invention proposes a computing method including an algorithmfor generating a voxelwise probability map of a specific tissue or tumorcharacteristic for the first subject from the first subject's registeredimaging dataset including the molecular and/or structural imaging datafor the first subject. The computing method includes the step ofmatching the registered ones of the molecular and/or structural imagingdata for the first subject to a dataset from the established orconstructed classifier or biomarker library obtained frompopulation-based information for the molecular and/or structural imaging(and/or other structural imaging) data for the second subjects and otherinformation (such as clinical and demographic data or the biopsytissue-based data) associated with the molecular and/or structuralimaging data for the second subjects. The method provides direct biopsytissue-based evidence (i.e., a large amount of the biopsy tissue-baseddata in the dataset or database of big data) for a medical or biologicaltest or diagnosis of tissues or organs of the first subject and showsheterogeneity within a single tumor focus with high sensitivity andspecificity.

The invention also proposes an objective to provide a method of forminga probability change map based on imaging data of a first subject beforeand after a medical treatment. The imaging data for the first subjectmay include (1) molecular and/or structural imaging data, such as MRIparameters, CT parameters, PET parameters, SPECT parameters, micro-PETparameters, micro-SPECT parameters, Raman parameters, and/or BLOparameters, and/or (2) other structural imaging data, such as from CTand/or ultrasound images. The method may build a dataset or database ofbig data containing molecular and/or structural imaging (and/or otherstructural imaging) data for multiple second subjects and biopsytissue-based data associated with the molecular and/or structuralimaging data for the second subjects. A classifier or biomarker librarymay be constructed or established from the dataset or database of bigdata. The invention proposes a computing method including an algorithmfor generating a probability change map of a specific tissue or tumorcharacteristic for the first subject from the first subject's molecularand/or structural imaging (and/or other structural imaging) data beforeand after the medical treatment. The computing method includes matchingthe registered ones of the molecular and/or structural imaging (and/orother structural imaging) data of the first subject before and after themedical treatment in the first subject's registered (multi-parametric)image dataset to the established or constructed classifier or biomarkerlibrary. The method matches the molecular and/or structural imaging(and/or other structural imaging) data for the first subject to theestablished or constructed classifier or biomarker library derived fromdirect biopsy tissue-based evidence (i.e., a large amount of the biopsytissue-based data in the dataset or database of big data) to obtain thechange of probabilities for the response and/or progression of themedical treatment and show heterogeneity of the response and/orprogression within a single tumor focus with high sensitivity andspecificity. The invention provides a method for effectively and timelyevaluating the effectiveness of the medical treatment, such asneoadjuvant chemotherapy for breast cancer, or radiation treatment forprostate cancer.

The invention also proposes an objective to provide a method forcollecting data for an image-tissue-clinical database for cancers.

The invention also proposes an objective to apply a big data technologyto build a probability map from multi-parameter molecular and/orstructural imaging data, including MRI parameters, PET parameters, SPECTparameters, micro-PET parameters, micro-SPECT parameters, Ramanparameters, and/or BLO parameters, and/or from other imaging data,including data from CT and/or ultrasound images. The invention providesa non-invasive method (such as molecular and/or structural imagingmethods, for example, MRI, Raman imaging, CT imaging) to diagnose aspecific tissue characteristic, such as breast cancer cells or prostatecancer cells, with better resolution (resolution size is 50% smaller, or25% smaller than the current resolution capability), and with a higherconfidence level. With data accumulated in the dataset or database ofbig data, the confidence level (for example, percentage of accuratediagnosis of a specific cancer cell) can be greater than 90%, or 95%,and eventually, greater than 99%.

The invention also proposes an objective to apply a big data technologyto build a probability change map from imaging data before and after atreatment. The imaging data may include (1) molecular and structuralimaging data, including MRI parameters, CT parameters, PET parameters,SPECT parameters, micro-PET parameters, micro-SPECT parameters, Ramanparameters, and/or BLO parameters, and/or (2) other structural imagingdata, including data from CT and/or ultrasound images. The inventionprovides a method for effectively and timely evaluating theeffectiveness of a treatment, such as neoadjuvant chemotherapy forbreast cancer or radiation treatment for prostate cancer.

In order to achieve the above objectives, the invention may provide amethod of forming a probability map composed of multiple computationvoxels with the same size or volume. The method may include thefollowing steps. First, a big data database (or called a database of bigdata) including multiple data sets is created. Each of the data sets inthe big data database may include a first set of information data, whichmay be obtained by a non-invasive method or a less-invasive method (ascompared to a method used to obtain the following second set ofinformation data), may be obtained more easily (than the method used toobtain the following second set of information data), or may provideinformation, obtained by a non-invasive method, for a specific tissue,to be biopsied or to be obtained by an invasive method, of an organ(e.g., prostate or breast) of a subject with a spatial volume covering,e.g., less than 10% or even less than 1% of the spatial volume of theorgan of the subject. The organ of the subject, for example, may be theprostate or breast of a human patient. The first set of data informationmay include measures of molecular and/or structural imaging parameters,such as measures of MRI parameters and/or CT parameters, and/or otherstructural imaging parameters, such as from CT and/or ultrasound images,for a volume and location of the specific tissue to be biopsied (e.g.,prostate or breast) from the organ of the subject. The “other structuralimaging parameters” may not be mentioned hereinafter. Each of themolecular and/or structural imaging parameters for the specific tissuemay have a measure calculated based on an average of measures, for saideach of the molecular and/or structural imaging parameters, obtainedfrom corresponding registered regions, portions, locations or volumes ofinterest of multiple molecular and/or structural images, such as MRIslices, PET slices, or SPECT images, registered to or aligned withrespective regions, portions, locations or volumes of interest of thespecific tissue to be biopsied. The combination of the registeredregions, portions, locations or volumes of interest of the molecularand/or structural images may have a total volume covering andsubstantially equaling the total volume of the specific tissue to bebiopsied. Each of the data sets in the big data database may furtherinclude the second set of information data, which may be obtained by aninvasive method or a more-invasive method (as compared to the methodused to obtain the above first set of information data), may be obtainedwith more difficulty (as compared to the method used to obtain the abovefirst set of information data), or may provide information for thespecific tissue, having been biopsied or obtained by an invasive method,of the organ of the subject. The second set of information data mayprovide information data with decisive, conclusive results for a betterjudgment or decision making. For example, the second set of informationdata may include a biopsy result, data or information (i.e., pathologistdiagnosis, for example cancer or no cancer) for the biopsied specifictissue. Each of the data sets in the big data database may also include:(1) dimensions related to molecular and/or structural imaging for theparameters, such as the thickness T of an MRI slice and the size of anMRI voxel of the MRI slice, including the width of the MRI voxel and thethickness or height of the MRI voxel (which may be the same as thethickness T of the MRI slice), (2) clinical data (e.g., age and sex ofthe patient and/or Gleason score of a prostate cancer) associated withthe biopsied specific tissue and/or the subject, and (3) risk factorsfor cancer associated with the subject (such as smoking history, sunexposure, premalignant lesions, and/or gene). For example, if thebiopsied specific tissue is obtained by a needle, the biopsied specifictissue is cylinder-shaped with a diameter or radius Rn (that is, aninner diameter or radius of the needle) and a height tT normalized tothe thickness T of the MRI slice. The invention proposes a method totransform the volume of the cylinder-shaped biopsied specific tissue (orVolume of Interest (VOI), which is π×Rn²×tT) into other shapes for easyor meaningful computing purposes, for medical instrumentation purposes,or for clearer final data presentation purposes. For example, the longcylinder of the biopsy specific tissue (with radius Rn and height tT)may be transformed into a planar cylinder (with radius Rw, which is theradius Rn multiplied by the square root of the number of the MRI sliceshaving the specific tissue to be biopsied extend therethrough) to matchthe MRI slice thickness T. The information of the radius Rw of theplanner cylinder, which has a volume the same or about the same as thevolume of the biopsied specific tissue, i.e., VOI, and has a height ofthe MRI slice thickness T, is used to define the size (e.g., the radius)of a moving window in calculating a probability map for a patient (e.g.,human). The invention proposes that, for each of the data sets, thevolume of the biopsy specific tissue, i.e., VOI, may be substantiallyequal to the volume of the moving window to be used in calculatingprobability maps. In other words, the volume of the biopsy specifictissue, i.e., VOI, defines the size of the moving window to be used incalculating probability maps. The concept of obtaining a feature size(e.g., the radius) of the moving window to be used in calculating aprobability map for an MRI slice is disclosed as above mentioned.Statistically, the moving window may be determined with the radius Rw(i.e., feature size), perpendicular to a thickness of the moving window,based on a statistical distribution or average of the radii Rw(calculated from VOIs) associated with a subset data from the big datadatabase. Next, a classifier for an event such as biopsy-diagnosedtissue characteristic for specific cancerous cells or occurrence ofprostate cancer or breast cancer is created based on the subset dataassociated with the event from the big data database. The subset datamay be obtained from all data associated with the given event. Aclassifier or biomarker library can be constructed or obtained usingstatistical methods, correlation methods, big data methods, and/orlearning and training methods.

After the big data database and the classifier are created orconstructed, an image of a patient, such as MRI slice image (i.e., amolecular image) or other suitable image, is obtained by a device orsystem such as MRI system. Furthermore, based on the feature size, e.g.,the radius Rw, of the moving window obtained from the subset data in thebig data database, the size of a computation voxel, which becomes thebasic unit of the probability map, is defined. If the moving window iscircular, the biggest square inscribed in the moving window is thendefined. Next, the biggest square is divided into n² small squares eachhaving a width Wsq, where n is an integer, such as 2, 3, 4, 5, 6, ormore than 6. The divided squares define the size and shape of thecomputation voxels in the probability map for the image of the patient.The moving window may move across the patient's image at a regular stepor interval of a fixed distance, e.g., substantially equal to the widthWsq of the computation voxels. A stop of the moving window overlaps theneighboring stop of the moving window. Alternatively, the biggest squaremay be divided into n rectangles each having a width Wrec and a lengthLrec, where n is an integer, such as 2, 3, 4, 5, 6, 7, 8, or more than8. The divided rectangles define the size and shape of the computationvoxels in the probability map for the image of the patient. The movingwindow may move across the patient's image at a regular step or intervalof a fixed distance, e.g., substantially equal to the width of thecomputation voxels (i.e., the width Wrec), in the x direction and at aregular step or interval of a fixed distance, e.g., substantially equalto the length of computation voxels (i.e., the length Lrec), in the ydirection. A stop of the moving window overlaps the neighboring stop ofthe moving window. In an alternative embodiment, each of the stops ofthe moving window may have a width, length or diameter less than theside length (e.g., the width or length) of voxels, such as defined by aresolution of a MRI system, in the image of the patient.

After the size and shape of the computation voxel is obtained ordefined, the stepping of the moving window and the overlapping betweentwo neighboring stops of the moving window can then be determined.Measures of specific imaging parameters for each stop of the movingwindow are obtained from the patient's molecular and/or structuralimaging information or image. The specific imaging parameters mayinclude molecular and/or structural imaging parameters, such as MRIparameters, PET parameters, SPECT parameters, micro-PET parameters,micro-SPECT parameters, Raman parameters, and/or BLO parameters, and/orother imaging parameters, such as CT parameters and/or ultrasoundimaging parameters. Each of the specific imaging parameters for eachstop of the moving window may have a measure calculated based on anaverage of measures, for said each of the specific imaging parameters,for voxels of the patient's image inside said each stop of the movingwindow. Some voxels of the patient's image may be only partially insidesaid each stop of the moving window. The average, for example, may beobtained from the measures, in said each stop of the moving window, eachweighed by its area proportion of an area of the voxel for said eachmeasure to an area of said each stop of the moving window. A registered(multi-parametric) image dataset may be created for the patient toinclude multiple imaging parameters, such as molecular parameters and/orother imaging parameters, obtained from various equipment, machines, ordevices, at a defined time-point (e.g., specific date) or in a timerange (e.g., within five days after treatment). Each of the imageparameters in the patient's registered (multi-parametric) image datasetrequires alignment or registration. The registration can be done by, forexamples, using unique anatomical marks, structures, tissues, geometry,shapes or using mathematical algorithms and computer patternrecognition.

Next, the specific imaging parameters for each stop of the moving windowmay be reduced using, e.g., subset selection, aggregation, anddimensionality reduction into a parameter set for said each stop of themoving window. In other words, the parameter set includes measures forindependent imaging parameters. The imaging parameters selected in theparameter set may have multiple types, such as two types, more than twotypes, more than three types, or more than four types, (statistically)independent from each other or one another, or may have a single type.For example, the imaging parameters selected in the parameter set mayinclude (a) MRI parameters and PET parameters, (b) MRI parameters andSPET parameters, (c) MRI parameters and CT parameters, (d) MRIparameters and ultrasound imaging parameters, (e) Raman imagingparameters and CT parameters, (f) Raman imaging parameters andultrasound imaging parameters, (g) MRI parameters, PET parameters, andultrasound imaging parameters, or (h) MRI parameters, PET parameters,and CT parameters.

Next, the parameter set for each stop of the moving window is matched tothe classifier to obtain a probability PW of the event for said eachstop of the moving window. This invention discloses an algorithm tocompute a probability of the event for each of the computation voxelsfrom the probabilities PWs of the event for the stops of the movingwindow covering said each of the computation voxels, as described in thefollowing steps ST1-ST11. In the step ST1, a first probability PV1 foreach of the computation voxels is calculated or assumed based on anaverage of the probabilities PWs of the event for the stops of themoving window overlapping said each of the computation voxels. In thestep ST2, a first probability guess PG1 for each stop of the movingwindow is calculated by averaging the first probabilities PV1s (obtainedin the step ST1) of all the computation voxels inside said each stop ofthe moving widow. In the step ST3, the first probability guess PG1 foreach stop of the moving window is compared with the probability PW ofthe event for said each stop of the moving window by subtracting theprobability PW of the event from the first probability guess PG1 forsaid each stop of the moving window so that a first difference DW1(DW1=PG1−PW) between the first probability guess PG1 and the probabilityPW of the event for said each stop of the moving window is obtained. Inthe step ST4, a first comparison is performed to determine whether theabsolute value of the first difference DW1 for each stop of the movingwindow is less than or equal to a preset threshold error. If any one ofthe absolute values of all the first differences DW1s is greater thanthe preset threshold error, the step ST5 continues. If the absolutevalues of all the first differences DW1s are less than or equal to thepreset threshold error, the step ST11 continues. In the step ST5, afirst error correction factor (ECF1) for each of the computation voxelsis calculated by summing error correction contributions from the stopsof the moving window overlapping said each of the computation voxels.For example, if there are four stops of the moving window overlappingone of the computation voxels, the error correction contribution fromeach of the four stops to said one of the computation voxels iscalculated by obtaining an area ratio of an overlapped area between saidone of the computation voxels and said each of the four stops to an areaof the biggest square inscribed in said each of the four stops, and thenmultiplying the first difference DW1 for said each of the four stops bythe area ratio. In the step ST6, a second probability PV2 for each ofthe computation voxels is calculated by subtracting the first errorcorrection factor ECF1 for said each of the computation voxels from thefirst probability PV1 for said each of the computation voxels(PV2=PV1−ECF1). In the step ST7, a second probability guess PG2 for eachstop of the moving window is calculated by averaging the secondprobabilities PV2s (obtained in the step ST6) of all the computationvoxels inside said each stop of the moving widow. In the step ST8, thesecond probability guess PG2 for each stop of the moving window iscompared with the probability PW of the event for said each stop of themoving window by subtracting the probability PW of the event from thesecond probability guess PG2 for said each stop of the moving window sothat a second difference DW2 (DW2=PG2−PW) between the second probabilityguess PG2 and the probability PW of the event for said each stop of themoving window is obtained. In the step S9, a second comparison isperformed to determine whether the absolute value of the seconddifference DW2 for each stop of the moving window is less than or equalthe preset threshold error. If any one of the absolute values of all thesecond differences DW2s is greater than the preset threshold error, thestep ST10 continues. If the absolute values of all the seconddifferences DW2s are less than or equal to the preset threshold error,the step ST11 continues. In the step ST10, the steps ST5-ST9 arerepeated or iterated, using the newly obtained the n^(th) difference DWnbetween the n^(th) probability guess PGn and the probability PW of theevent for each stop of the moving window for calculation in the(n+1)^(th) iteration, until the absolute value of the (n+1)^(th)difference DW(n+1) for said each stop of the moving window is equal toor less than the preset threshold error (Note: PV1, PG1 and DW1 for thefirst iteration, ECF1, PV2, PG2 and DW2 for the second iteration, andECF(n−1), PVn, PGn and DWn for the n^(th) iteration). In the step ST11,the first probabilities PV1s in the first iteration, i.e., the stepsST1-ST4, the second probabilities PV2s in the second iteration, i.e.,the steps ST5-ST9, or the (n+1)^(th) probabilities PV(n+1)s in the(n+1)^(th) iteration, i.e., the step ST10, are used to form theprobability map. The probabilities of the event for the computationvoxels are obtained using the above method, procedure or algorithm,based on overlapped stops of the moving window, to form the probabilitymap of the event for the image of the patient (e.g., patient's MRIslice) having imaging information (e.g., molecular and/or structuralimaging information). The above process using the moving window in thex-y direction would create a two-dimensional (2D) probability map. Inorder to obtain a three-dimensional (3D) probability map, the aboveprocesses for all MRI slices of the patient would be performed in the zdirection in addition to the x-y direction.

After the probability map is obtained, the patient may undergo a biopsyto obtain a tissue sample from an organ of the patient (i.e., that isshown on the image of the patient) at a suspected region of theprobability map. The tissue sample is then sent to be examined bypathology. Based on the pathology diagnosis of the tissue sample, it canbe determined whether the probabilities for the suspected region of theprobability map are precise or not. In the invention, the probabilitymap may provide information for a portion or all of the organ of thepatient with a spatial volume greater than 80% or even 90% of thespatial volume of the organ, than the spatial volume of the tissuesample (which may be less than 10% or even 1% of the spatial volume ofthe organ), and/or than the spatial volume of the specific tissueprovided for the first and second sets of information data in the bigdata database.

In order to further achieve the above objectives, the invention mayprovide a method of forming a probability-change map of theaforementioned event for a treatment. The method is described in thefollowing steps: (1) obtaining probabilities of the event for respectivestops of the moving window on pre-treatment and post-treatment images(e.g., MRI slice) of a patient in accordance with the methods andprocedures as described above, wherein the probability of the event foreach stop of the moving window on the pre-treatment image of the patientcan be obtained based on measures for molecular and/or structuralimaging parameters (and/or other imaging parameters) taken before thetreatment; similarly, the probability of the event for each stop of themoving window on the post-treatment image of the patient can be obtainedbased on measures for the molecular and/or structural imaging parameters(and/or other imaging parameters) taken after the treatment; all themeasures for the molecular and/or structural imaging parameters (orother imaging parameters) taken before the treatment are obtained fromthe registered (multi-parametric) image dataset for the pre-treatmentimage; all the measures for the molecular and/or structural imagingparameters (or other imaging parameters) taken after the treatment areobtained from the registered (multi-parametric) image dataset for thepost-treatment image; (2) calculating a probability change PMC betweenthe probabilities of the event before and after the treatment for eachstop of the moving window; and (3) calculating a probability change PVCof each of computation voxels associated with the treatment based on theprobability changes PMCs for the stops of the moving window by followingthe methods and procedures described above for calculating theprobability of each of computation voxels from the probabilities of thestops of the moving window, that is, the probabilities are replaced withthe probability changes PMCs for the stops of the moving window toperform the above methods to calculate the probability changes PVCs ofthe computation voxels. The obtained probability changes PVCs for thecomputation voxels then compose a probability-change map of the eventfor the treatment. Performing the above processes for all images (e.g.,MRI slices) in the z direction, a 3D probability-change map of the eventfor the treatment can be obtained.

In general, the invention proposes an objective to provide a method,system (including, e.g., hardware, devices, computers, processors,software, and/or tools), device, tool, software or hardware for formingor generating a decision data map, e.g., a probability map, based onfirst data of a first type (e.g., first measures of MRI parameters) froma first subject such as a human or an animal. The method, system,device, tool, software or hardware may include building a database ofbig data (or call a big data database) including second data of thefirst type (e.g., second measures of the MRI parameters) from apopulation of second subjects and third data of a second type (e.g.,biopsy results, data or information) from the population of secondsubjects. The third data of the second type may provide information datawith decisive, conclusive results for a better judgment or decisionmaking (e.g., a patient whether to have a specific cancer or not). Thesecond and third data of the first and second types from each of thesecond subjects in the population, for example, may be obtained from acommon portion of said each of the second subjects in the population. Aclassifier related to a decision-making characteristic (e.g., occurrenceof prostate cancer or breast cancer) is established or constructed fromthe database of big data. The method, system, device, tool, software orhardware may provide a computing method including an algorithm forgenerating the decision data map with finer voxels associated with thedecision-making characteristic for the first subject by matching thefirst data of the first type to the established or constructedclassifier. The method, system, device, tool, software or hardwareprovides a decisive-and-conclusive-result-based evidence for a betterjudgment or decision making based on the first data of the first type(without any data of the second type from the first subject). The seconddata of the first type, for example, may be obtained by a non-invasivemethod or a less-invasive method (as compared to a method used to obtainthe third data of the second type) or may be obtained more easily (ascompared to the method used to obtain the third data of the secondtype). The second data of the first type may provide information,obtained by, e.g., a non-invasive method, for a specific tissue, to bebiopsied or to be obtained by an invasive method, of an organ of eachsecond subject with a spatial volume covering, e.g., less than 10% oreven less than 1% of the spatial volume of the organ of said each secondsubject. The second data of the first type may include measures or dataof molecular imaging (and/or other imaging) parameters, such as measuresof MRI parameters and/or CT data. The third data of the second type, forexample, may be obtained by an invasive method or a more-invasive method(as compared to the method used to obtain the second data of the firsttype) or may be harder to obtain (as compared to the method used toobtain the second data of the first type). The third data of the secondtype may provide information for the specific tissue, having beenbiopsied or obtained by an invasive method, of the organ of each secondsubject. The third data of the second type may include biopsy results,data or information (for example a patient whether to have a cancer ornot) for the biopsied specific tissues of the second subjects in thepopulation. The decision making may be related to, for example, adecision on whether the first subject has cancerous cells or not. Thisinvention provides a method to make better decision, judgment orconclusion for the first subject (a patient, for example) based on thefirst data of the first type, without any data of the second type fromthe first subject. This invention provides a method to use MR1 imagingdata to directly diagnose whether an organ or tissue (such as breast orprostate) of the first subject has cancerous cells or not withoutperforming a biopsy test for the first subject. In general, thisinvention provides a method to make decisive conclusion, with 90% orover 90% of accuracy (or confidence level), or with 95% or over 95% ofaccuracy (or confidence level), or eventually, with 99% or over 99% ofaccuracy (or confidence level). Furthermore, the invention provides aprobability map with its spatial resolution of computation voxels thatis 75%, 50% or 25%, in one dimension (1D), smaller than that ofmachine-defined voxels of an image created by the current availablemethod. The machine-defined voxels of the image, for example, may bevoxels of an MRI image.

These, as well as other components, steps, features, benefits, andadvantages of the present disclosure, will now become clear from areview of the following detailed description of illustrativeembodiments, the accompanying drawings, and the claims.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings disclose illustrative embodiments of the presentdisclosure. They do not set forth all embodiments. Other embodiments maybe used in addition or instead. Details that may be apparent orunnecessary may be omitted to save space or for more effectiveillustration. Conversely, some embodiments may be practiced without allof the details that are disclosed. When the same reference number orreference indicator appears in different drawings, it may refer to thesame or like components or steps.

Aspects of the disclosure may be more fully understood from thefollowing description when read together with the accompanying drawings,which are to be regarded as illustrative in nature, and not as limiting.The drawings are not necessarily to scale, emphasis instead being placedon the principles of the disclosure. In the drawings:

FIG. 1A is a schematic drawing showing a “Big Data” probability mapcreation in accordance with an embodiment of the present invention;

FIGS. 1B-1G show a subset data table in accordance with an embodiment ofthe present invention;

FIGS. 1H-1M show a subset data table in accordance with an embodiment ofthe present invention;

FIG. 2A is a schematic drawing showing a biopsy tissue and multiple MRIslices registered to the biopsy tissue in accordance with an embodimentof the present invention;

FIG. 2B is a schematic drawing of a MRI slice in accordance with anembodiment of the present invention;

FIG. 2C is a schematic drawing showing multiple voxels of a MRI slicecovered by a region of interest (ROI) on the MRI slice in accordancewith an embodiment of the present invention;

FIG. 2D shows a data table in accordance with an embodiment of thepresent invention;

FIG. 2E shows a planar cylinder transformed from a long cylinder of abiopsied tissue in accordance with an embodiment of the presentinvention;

FIG. 3A is a schematic drawing showing a circular window and atwo-by-two grid array within a square inscribed in the circular windowin accordance with an embodiment of the present invention;

FIG. 3B is a schematic drawing showing a circular window and athree-by-three grid array within a square inscribed in the circularwindow in accordance with an embodiment of the present invention;

FIG. 3C is a schematic drawing showing a circular window and afour-by-four grid array within a square inscribed in the circular windowin accordance with an embodiment of the present invention;

FIG. 4 is a flow chart illustrating a computing method of generating orforming a probability map in accordance with an embodiment of thepresent invention;

FIG. 5 shows a MRI slice showing a prostate, as well as a computationregion on the MRI slice, in accordance with an embodiment of the presentinvention;

FIG. 6A is a schematic drawing showing a circular window moving across acomputation region of a MRI slice in accordance with an embodiment ofthe present invention;

FIG. 6B shows a square inscribed in a circular window having a corneraligned with a corner of a computation region of a MRI slice inaccordance with an embodiment of the present invention;

FIG. 7A is a schematic drawing showing multiple voxels of a MRI slicecovered by a circular window in accordance with an embodiment of thepresent invention;

FIG. 7B shows a data table in accordance with an embodiment of thepresent invention;

FIG. 8 is a flow chart depicting an algorithm for generating aprobability map in accordance with an embodiment of the presentinvention;

FIG. 9 shows a computation region defined with nine computation voxelsfor a probability map in accordance with an embodiment of the presentinvention;

FIGS. 10A, 10C, 10E, and 10G show four stops of a circular movingwindow, each of which includes four non-overlapped small squares, inaccordance with an embodiment of the present invention;

FIGS. 10B, 10D, 10F, and 10H show a circular window moving across acomputation region defined with nine computation voxels in accordancewith an embodiment of the present invention;

FIGS. 11A, 11B, and 11C show initial probabilities for computationvoxels, updated probabilities for the computation voxels, and optimalprobabilities for the computation voxels, respectively, in accordancewith an embodiment of the present invention;

FIG. 12 shows a computation region defined with thirty-six computationvoxels for a probability map in accordance with an embodiment of thepresent invention;

FIGS. 13A, 13C, 13E, 13G, 14A, 14C, 14E, 14G, 15A, 15C, 15E, 15G, 16A,16C, 16E, and 16G show sixteen stops of a circular moving window, eachof which includes nine non-overlapped small squares, in accordance withan embodiment of the present invention;

FIGS. 13B, 13D, 13F, 13H, 14B, 14D, 14F, 14H, 15B, 15D, 15F, 15H, 16B,16D, 16F, and 16H show a circular window moving across a computationregion defined with thirty-six computation voxels in accordance with anembodiment of the present invention;

FIGS. 17A, 17B, and 17C show initial probabilities for computationvoxels, updated probabilities for the computation voxels, and optimalprobabilities for the computation voxels, respectively, in accordancewith an embodiment of the present invention;

FIGS. 18A-18C show three probability maps;

FIG. 18D shows a composite probability image or map;

FIG. 19 shows a MRI slice showing a breast, as well as a computationregion on the MRI slice, in accordance with an embodiment of the presentinvention;

FIGS. 20A-20R show a description of various parameters (“parametercharts” and “biomarker” charts could be used to explain many items thatcould be included in a big data database, this would include theontologies, mRNA, next generation sequencing, etc., and exact data in“subset” databases could then be more specific and more easily generateddata);

FIG. 21 is a flow chart depicting a method of evaluating, identifying,or determining the effect of a treatment (e.g., neoadjuvant chemotherapyor minimally invasive treatment of prostate cancer) or a drug used inthe treatment on a subject in accordance with an embodiment of thepresent invention;

FIG. 22 is a flow chart depicting a method of evaluating, identifying,or determining the effect of a treatment or a drug used in the treatmenton a subject in accordance with an embodiment of the present invention;

FIG. 23 is a flow chart depicting a method of evaluating, identifying,or determining the effect of a treatment or a drug used in the treatmenton a subject in accordance with an embodiment of the present invention;and

FIG. 24 is a diagram showing two Gaussian curves of two given differentgroups with respect to parameter measures.

While certain embodiments are depicted in the drawings, one skilled inthe art will appreciate that the embodiments depicted are illustrativeand that variations of those shown, as well as other embodimentsdescribed herein, may be envisioned and practiced within the scope ofthe present disclosure.

DETAILED DESCRIPTION OF THE INVENTION

Illustrative embodiments are now described. Other embodiments may beused in addition or instead. Details that may be apparent or unnecessarymay be omitted to save space or for a more effective presentation.Conversely, some embodiments may be practiced without all of the detailsthat are disclosed.

Computing methods described in the present invention may be performed onany type of image, such as molecular and structural image (e.g., MRIimage, CT image, PET image, SPECT image, micro-PET, micro-SPECT, Ramanimage, or bioluminescence optical (BLO) image), structural image (e.g.,CT image or ultrasound image), fluoroscopy image, structure/tissueimage, optical image, infrared image, X-ray image, or any combination ofthese types of images, based on a registered (multi-parametric) imagedataset for the image. The registered (multi-parametric) image datasetmay include multiple imaging data or parameters obtained from one ormore modalities, such as MRI, PET, SPECT, CT, fluoroscopy, ultrasoundimaging, BLO imaging, micro-PET, micro-SPECT, Raman imaging,structure/tissue imaging, optical imaging, infrared imaging, and/orX-ray imaging. For a patient, the registered (multi-parametric) imagedataset may be created by aligning or registering in space allparameters obtained from different times or from various machines.Methods in first, second and third embodiments of the invention may beperformed on a MRI image based on the registered (multi-parametric)image dataset, including, e.g., MRI parameters and/or PET parameters,for the MRI image.

Referring to FIG. 1A, a big data database 70 is created to includemultiple data sets, each of which may include: (1) a first set ofinformation data, which may be obtained by a non-invasive method or aless-invasive method (as compared to a method used to obtain thefollowing second set of information data), wherein the first set of datainformation may include measures for multiple imaging parameters,including, e.g., molecular and structural imaging parameters (such asMRI parameters, CT parameters, PET parameters, SPECT parameters,micro-PET parameters, micro-SPECT parameters, Raman parameters, and/orBLO parameters) and/or other structural imaging data (such as from CTand/or ultrasound images), for a volume and location of a tissue to bebiopsied (e.g., prostate or breast) from a subject such as human oranimal, (2) combinations each of specific some of the imagingparameters, (3) dimensions related to imaging parameters (e.g.,molecular and structural imaging parameters), such as the thickness T ofan MRI slice and the size of an MRI voxel of the MRI slice, includingthe width or side length of the MRI voxel and the thickness or height ofthe MRI voxel (which may be substantially equal to the thickness T ofthe MRI slice), (4) a second set of information data obtained by aninvasive method or a more-invasive method (as compared to the methodused to obtain the first set of information data), wherein the secondset of the information data may include tissue-based information from abiopsy performed on the subject, (5) clinical data (e.g., age and sex ofthe subject and/or Gleason score of a prostate cancer) associated withthe biopsied tissue and/or the subject, and (6) risk factors for cancerassociated with the subject.

Some or all of the subjects for creating the big data database 70 mayhave been subjected to a treatment such as neoadjuvant chemotherapy or(preoperative) radiation therapy. Alternatively, some or all of thesubjects for creating the big data database 70 are not subjected to atreatment such as neoadjuvant chemotherapy or (preoperative) radiationtherapy. The imaging parameters in each of the data sets of the big datadatabase 70 may be obtained from different modalities, including two ormore of the following: MRI, PET, SPECT, CT, fluoroscopy, ultrasoundimaging, BLO imaging, micro-PET, micro-SPECT, and Raman imaging.Accordingly, the imaging parameters in each of the data sets of the bigdata database 70 may include four or more types of MRI parametersdepicted in FIGS. 20A-20H, one or more types of PET parameters depictedin FIG. 20I, one or more types of heterogeneity features depicted inFIG. 20J, and other parameters depicted in FIG. 20K. Alternatively, thefirst set of information data may only include a type of imagingparameter (such as T1 mapping). In each of the data sets of the big datadatabase 70, each of the imaging parameters (such as T1 mapping) for thetissue to be biopsied may have a measure calculated based on an averageof measures, for said each of the imaging parameters, for multipleregions, portions, locations or volumes of interest of multipleregistered images (such as MRI slices) registered to or aligned withrespective regions, portions, locations or volumes of the tissue to bebiopsied, wherein all of the regions, portions, locations or volumes ofinterest of the registered images may have a total volume covering andsubstantially equaling the volume of the tissue to be biopsied. Thenumber of the registered images for the tissue to be biopsied may begreater than or equal to 2, 5 or 10.

In the case of the biopsied tissue obtained by a needle, the biopsiedtissue may be long cylinder-shaped with a radius Rn, which issubstantially equal to an inner radius of the needle, and a height tTnormalized to the thickness T of the MRI slice. In the invention, thevolume of the long cylinder-shaped biopsied tissue may be transformedinto another shape, which may have a volume the same or about the sameas the volume of the long cylinder-shaped biopsied tissue (or Volume ofInterest, VOI, which may be π×Rn²×tT), for easy or meaningful computingpurposes, for medical instrumentation purposes, or for clearer finaldata presentation purposes. For example, the long cylinder of thebiopsied tissue with the radius Rn and height tT may be transformed intoa planar cylinder to match the MRI slice thickness T. The planarcylinder, for example, may have a height equal to the MRI slicethickness T, a radius Rw equal to the radius Rn multiplied by the squareroot of the number of the registered images, and a volume the same orabout the same as the volume of the biopsied tissue, i.e., VOI. Theradius Rw of the planner cylinder is used to define the size (e.g., theradius Rm) of a moving window MW in calculating a probability map for apatient (e.g., human). In the invention, the volume of the biopsiedtissue, i.e., VOI, for each of the data sets, for example, may besubstantially equal to the volume of the moving window MW to be used incalculating probability maps. In other words, the volume of the biopsiedtissue, i.e., VOI, defines the size of the moving window MW to be usedin calculating probability maps. Statistically, the moving window MW maybe determined with the radius Rm, perpendicular to a thickness of themoving window MW, based on the statistical distribution or average ofthe radii Rw (calculated from multiple VOIs) associated with a subsetdata (e.g., the following subset data DB-1 or DB-2) from the big datadatabase 70.

The tissue-based information in each of the data sets of the big datadatabase 70 may include (1) a biopsy result, data, information (i.e.,pathologist diagnosis, for example cancer or no cancer) for the biopsiedtissue, (2) mRNA data or expression patterns, (3) DNA data or mutationpatterns (including that obtained from next generation sequencing), (4)ontologies, (5) biopsy related feature size or volume (including theradius Rn of the biopsied tissue, the volume of the biopsied tissue(i.e., VOI), and/or the height tT of the biopsied tissue), and (6) otherhistological and biomarker findings such as necrosis, apoptosis,percentage of cancer, increased hypoxia, vascular reorganization, andreceptor expression levels such as estrogen, progesterone, HER2, andEPGR receptors. For example, regarding the tissue-based information ofthe big data database 70, each of the data sets may include specificlong chain mRNA biomarkers from next generation sequencing that arepredictive of metastasis-free survival, such as HOTAIR, RP11-278L15.2-001, LINC00511-009, AC004231.2-001. The clinical data in each ofthe data sets of the big data database 70 may include the timing oftreatment, demographic data (e.g., age, sex, race, weight, family type,and residence of the subject), and TNM staging depicted in, e.g., FIGS.20N and 20O or FIGS. 20P, 20Q and 20R. Each of the data sets of the bigdata database 70 may further include information regarding neoadjuvantchemotherapy and/or information regarding (preoperative) radiationtherapy. Imaging protocol details, such as MRI magnet strength, pulsesequence parameters, PET dosing, time at PET imaging, may also beincluded in the big data database 70. The information regarding(preoperative) radiation therapy may include the type of radiation, thestrength of radiation, the total dose of radiation, the number offractions (depending on the type of cancer being treated), the durationof the fraction from start to finish, the dose of the fraction, theduration of the preoperative radiation therapy from start to finish, andthe type of machine used for the preoperative radiation therapy. Theinformation regarding neoadjuvant chemotherapy may include the givendrug(s), the number of cycles (i.e., the duration of the neoadjuvantchemotherapy from start to finish), the duration of the cycle from startto finish, and the frequency of the cycle.

Data of interest are selected from the big data database 70 into asubset, used to build a classifier CF. The subset from the big datadatabase 70 may be selected for a specific application, such as prostatecancer, breast cancer, breast cancer after neoadjuvant chemotherapy, orprostate cancer after radiation. In the case of the subset selected forprostate cancer, the subset may include data in a tissue-based orbiopsy-based subset data DB-1. In the case of the subset selected forbreast cancer, the subset may include data in a tissue-based orbiopsy-based subset data DB-2. Using suitable methods, such asstatistical methods, correlation methods, big data methods, and/orlearning and training methods, the classifier CF may be constructed orcreated based on a first group associated with a first data type orfeature (e.g., prostate cancer or breast cancer) in the subset, a secondgroup associated with a second data type or feature (e.g., non-prostatecancer or non-breast cancer) in the subset, and some or all of thevariables in the subset associated with the first and second groups.Accordingly, the classifier CF for an event, such as the first data typeor feature, may be created based on the subset associated with the eventfrom the big data database 70. The event may be a biopsy-diagnosedtissue characteristic, such as having specific cancerous cells, oroccurrence of prostate cancer or breast cancer.

After the database 70 and the classifier CF are created or constructed,a probability map, composed of multiple computation voxels with the samesize, is generated or constructed for, e.g., evaluating or determiningthe health status of a patient (e.g., human subject), the physicalcondition of an organ or other structure inside the patient's body, orthe patient's progress and therapeutic effectiveness by the stepsdescribed below. First, an image of the patient is obtained by a deviceor system, such as MRI system. The image of the patient, for example,may be a molecular image (e.g., MRI image, PET image, SPECT image,micro-PET image, micro-SPECT image, Raman image, or BLO image) or othersuitable image (e.g., CT image or ultrasound image). In addition, basedon the radius Rm of the moving window MW obtained from the subset, e.g.,the subset data DB-1 or DB-2, in the big data database 70, the size ofthe computation voxel, which becomes the basic unit of the probabilitymap, is defined.

If the moving window MW is circular, the biggest square inscribed in themoving window MW is then defined. Next, the biggest square inscribed inthe moving window MW is divided into n² small squares, i.e., cubes, eachhaving a width Wsq, where n is an integer, such as 2, 3, 4, 5, 6, ormore than 6. The divided squares define the size and shape of thecomputation voxels in the probability map for the image of the patient.For example, each of the computation voxels of the probability map maybe defined as a square, i.e., cube, having the width Wsq and a volumethe same or about the same as that of each of the divided squares. Themoving window MW may move across the image of the patient at a regularstep or interval of a fixed distance, e.g., substantially equal to thewidth Wsq (i.e., the width of the computation voxels), in the x and ydirections. A stop of the moving window MW overlaps the neighboring stopof the moving window MW.

Alternatively, the biggest square inscribed in the moving window MW maybe divided into n rectangles each having a width Wrec and a length Lrec,where n is an integer, such as 2, 3, 4, 5, 6, 7, 8, or more than 8. Thedivided rectangles define the size and shape of the computation voxelsin the probability map for the image of the patient. Each of thecomputation voxels of the probability map, for example, may be arectangle having the width Wrec, the length Lrec, and a volume the sameor about the same as that of each of the divided rectangles. The movingwindow MW may move across the patient's molecular image at a regularstep or interval of a fixed distance, e.g., substantially equal to thewidth Wrec (i.e., the width of the computation voxels), in the xdirection and at a regular step or interval of a fixed distance, e.g.,substantially equal to the length Lrec (i.e., the length of thecomputation voxels), in the y direction. A stop of the moving window MWoverlaps the neighboring stop of the moving window MW. In an alternativeembodiment, each of the stops of the moving window MW may have a width,length or diameter less than the side length (e.g., the width or length)of voxels in the image of the patient.

After the size and shape of the computation voxels are obtained ordefined, the stepping of the moving window MW and the overlappingbetween two neighboring stops of the moving window MW can then bedetermined. Measures of specific imaging parameters for each stop of themoving window MW may be obtained from the patient's image and/ordifferent parameter maps (e.g., MRI parameter map(s), PET parametermap(s) and/or CT parameter map(s)) registered to the patient's image.The specific imaging parameters may include two or more of thefollowing: MRI parameters, PET parameters, SPECT parameters, micro-PETparameters, micro-SPECT parameters, Raman parameters, BLO parameters, CTparameters, and ultrasound imaging parameters. Each of the specificimaging parameters for each stop of the moving window MW, for example,may have a measure calculated based on an average of measures, for saideach of the specific imaging parameters, for voxels of the patient'simage inside said each stop of the moving window MW. In the case thatsome voxels of the patient's image only partially inside that stop ofthe moving window MW, the average can be weighed by the area proportion.The specific imaging parameters of different modalities may be obtainedfrom registered image sets (or registered parameter maps), and rigid andnonrigid standard registration techniques may be used to get eachsection of anatomy into the same exact coordinate location on each ofthe registered (multi-parametric) image dataset.

A registered (multi-parametric) image dataset may be created for thepatient to include multiple registered images (including two or more ofthe following: MRI slice images, PET images, SPECT images, micro-PETimages, micro-SPECT images, Raman images, BLO images, CT images, andultrasound images) and/or corresponding imaging parameters (includingtwo or more of the following: MRI parameters, PET parameters, SPECTparameters, micro-PET parameters, micro-SPECT parameters, Ramanparameters, BLO parameters, CT parameters, and/or ultrasound imagingparameters) obtained from various equipment, machines, or devices orfrom a defined time point (e.g., specific date) or time range (e.g.,within five days after treatment). Each of the imaging parameters in thepatient's registered (multi-parametric) image dataset requires alignmentor registration. The registration can be done by, for examples, usingunique anatomical marks, structures, tissues, geometry, and/or shapes orusing mathematical algorithms and computer pattern recognition. Themeasures of the specific imaging parameters for each stop of the movingwindow MW, for example, may be obtained from the registered(multi-parametric) image dataset for the patient.

Next, the specific imaging parameters for each stop of the moving windowMW may be reduced using, e.g., subset selection, aggregation, anddimensionality reduction into a parameter set for said each stop of themoving window MW. In other words, the parameter set includes measuresfor independent imaging parameters. The imaging parameters used in theparameter set may have multiple types, such as two types, more than twotypes, more than three types, or more than four types, independent fromeach other or one another, or may have a single type. For example, theimaging parameters used in the parameter set may include (a) MRIparameters and PET parameters, (b) MRI parameters and SPET parameters,(c) MRI parameters and CT parameters, (d) MRI parameters and ultrasoundimaging parameters, (e) Raman imaging parameters and CT parameters, (f)Raman imaging parameters and ultrasound imaging parameters, (g) MRIparameters, PET parameters, and ultrasound imaging parameters, or (h)MRI parameters, PET parameters, and CT parameters.

Next, the parameter set for each stop of the moving window MW is matchedto the classifier CF to obtain a probability PW of the event for saideach stop of the moving window MW. After the probabilities PWs of theevent for the stops of the moving window MW arc obtained, an algorithmis performed based on the probabilities PWs of the event for the stopsof the moving window MW to compute probabilities of the event for thecomputation voxels, as mentioned in the following steps ST1-ST11. In thestep ST1, a first probability PV1 for each of the computation voxels,for example, may be calculated or assumed based on an average of theprobabilities PWs of the event for the stops of the moving window MWoverlapping or covering said each of the computation voxels. In the stepST2, a first probability guess PG1 for each stop of the moving window MWis calculated by averaging the first probabilities PV1s (obtained in thestep ST1) of all the computation voxels inside said each stop of themoving widow MW. In the step ST3, the first probability guess PG1 foreach stop of the moving window MW is compared with the probability PW ofthe event for said each stop of the moving window MW by, e.g.,subtracting the probability PW of the event from the first probabilityguess PG1 so that a first difference DW1 (DW1=PG1−PW) between the firstprobability guess PG1 and the probability PW of the event for said eachstop of the moving window MW is obtained. In the step ST4, a firstcomparison is performed to determine whether an absolute value of thefirst difference DW1 for each stop of the moving window MW is less thanor equal to a preset threshold error. If any one of the absolute valuesof all the first differences DW1s is greater than the preset thresholderror, the step ST5 continues. If the absolute values of all the firstdifferences DW1s arc less than or equal to the preset threshold error,the step ST11 continues. In the step ST5, a first error correctionfactor (ECF1) for each of the computation voxels is calculated by, e.g.,summing error correction contributions from the stops of the movingwindow MW overlapping or covering said each of the computation voxels.For example, if there are four stops of the moving window MW overlappingor covering one of the computation voxels, each of the error correctioncontributions to said one of the computation voxels is calculated byobtaining an area ratio of an overlapped area between said one of thecomputation voxels and a corresponding one of the four stops to an areaof the biggest square inscribed in the corresponding one of the fourstops, and then multiplying the first difference DW1 for thecorresponding one of the four stops by the area ratio. In the step ST6,a second probability PV2 for each of the computation voxels iscalculated by subtracting the first error correction factor ECF1 forsaid each of the computation voxels from the first probability PV1 forsaid each of the computation voxels (PV2=PV1−ECF1). In the step ST7, asecond probability guess PG2 for each stop of the moving window MW iscalculated by averaging the second probabilities PV2s (obtained in thestep ST6) of all the computation voxels inside said each stop of themoving widow MW. In the step ST8, the second probability guess PG2 foreach stop of the moving window MW is compared with the probability PW ofthe event for said each stop of the moving window MW by, e.g.,subtracting the probability PW of the event from the second probabilityguess PG2 so that a second difference DW2 (DW2=PG2−PW) between thesecond probability guess PG2 and the probability PW of the event forsaid each stop of the moving window MW is obtained. In the step S9, asecond comparison is performed to determine whether an absolute value ofthe second difference DW2 for each stop of the moving window MW is lessthan or equal the preset threshold error. If any one of the absolutevalues of all the second differences DW2s is greater than the presetthreshold error, the step ST10 continues. If the absolute values of allthe second differences DW2s are less than or equal to the presetthreshold error, the step ST11 continues. In the step ST10, the stepsST5-ST9 are repeated or iterated, using the newly obtained the n^(th)difference DWn between the n^(th) probability guess PGn and theprobability PW of the event for each stop of the moving window MW forcalculation in the (n+1)^(th) iteration, until an absolute value of the(n+1)^(th) difference DW(n+1) for each stop of the moving window MW isequal to or less than the preset threshold error (Note: PV1, PG1 and DW1for the first iteration, ECF1, PV2, PG2 and DW2 for the seconditeration, and ECF(n−1), PVn, PGn and DWn for the n^(th) iteration). Inthe step ST11, the first probabilities PV1s in the first iteration,i.e., the steps ST1-ST4, the second probabilities PV2s in the seconditeration, i.e., the steps ST5-ST9, or the (n+1)^(th) probabilitiesPV(n+1)s in the (n+1)^(th) iteration, i.e., the step ST10, are used toform the probability map. The probabilities of the event for thecomputation voxels are obtained using the above method, procedure oralgorithm, based on the overlapped stops of the moving window MW, toform the probability map of the event for the image (e.g., patient's MRIslice) for the patient having imaging information (e.g., molecularimaging information). The above process is performed to generate themoving window MW across the image in the x and y directions to create atwo-dimensional (2D) probability map. In order to obtain athree-dimensional (3D) probability map, the above process may be appliedto each of all images of the patient in the z direction perpendicular tothe x and y directions.

Description of Subset Data DB-1:

Referring to FIGS. 1B-1G, the tissue-based or biopsy-based subset dataDB-1 from the big data database 70 includes multiple data sets eachlisted in the corresponding one of its rows 2 through N, wherein thenumber of the data sets may be greater than 100, 1,000 or 10,000. Eachof the data sets in the subset data DB-1 may include: (1) measures forMRI parameters associated with a prostate biopsy tissue (i.e., biopsiedsample of the prostate) obtained from a subject (e.g., human), as shownin columns A-O; (2) measures for processed parameters associated withthe prostate biopsy tissue, as shown in columns P and Q; (3) a result orpathologist diagnosis of the prostate biopsy tissue, such as prostatecancer, normal tissue, or benign condition, as shown in a column R; (4)sample characters associated with the prostate biopsy tissue, as shownin columns S-X; (5) MRI characters associated with MRI slices registeredto respective regions, portions, locations or volumes of the prostatebiopsy tissue, as shown in columns Y, Z and AA; (6) clinical orpathology parameters associated with the prostate biopsy tissue or thesubject, as shown in columns AB-AN; and (7) personal informationassociated with the subject, as shown in columns AO-AR. Needles used toobtain the prostate biopsy tissues may have the same cross-sectionalshape (e.g., round shape or square shape) and the same inner diameter orwidth, e.g., ranging from, equal to or greater than 0.1 millimeters upto, equal to or less than 5 millimeters, and more preferably rangingfrom, equal to or greater than 1 millimeter up to, equal to or less than3 millimeters.

The MRI parameters in the columns A-O of the subset data DB-1 are T1mapping, T2 raw signal, T2 mapping, delta Ktrans (ΔKtrans), tau, DtIV1M, fp IV1M, ADC (high b-values), nADC (high b-values), R*, Ktransfrom Tofts Model (TM), Ktrans from Extended Tofts Model (ETM), Ktransfrom Shutterspeed Model (SSM), Ve from TM, and Ve from SSM. For moreinformation about the MRI parameters in the subset data DB-1, pleaserefer to FIGS. 20A through 20H. The processed parameter in the column Pof the subset data DB-1 is average Ve, obtained by averaging Ve from TMand Ve from SSM. The processed parameter in the column Q of the subsetdata DB-1 is average Ktrans, obtained by averaging Ktrans from TM,Ktrans from ETM, and Ktrans from SSM. All data can have normalizedvalues, such as z scores.

Measures in the respective columns T, U and V of the subset data DB-1are Gleason scores associated with the respective prostate biopsytissues and primary and secondary Gleason grades associated with theGleason scores; FIG. 20L briefly explains Gleason score, the primaryGleason grade, and the secondary Gleason grade. Measures in the column Wof the subset data DB-1 may be the diameters of the prostate biopsytissues, and the diameter of each of the prostate biopsy tissues may besubstantially equal to an inner diameter of a cylinder needle, throughwhich a circular or round hole passes for receiving said each of theprostate biopsy tissues. Alternatively, measures in the column W of thesubset data DB-1 may be the widths of the prostate biopsy tissues, andthe width of each of the prostate biopsy tissues may be substantiallyequal to an inner width of a needle, through which a square orrectangular hole passes for receiving said each of the prostate biopsytissues. The clinical or pathology parameters in the columns AB-AN ofthe subset data DB-1 are prostate specific antigen (PSA), PSA velocity,% free PSA, Histology subtype, location within a given anatomicalstructure of gland, tumor size, PRADS, pathological diagnosis (e.g.,Atypia, benign prostatic hypertrophy (BPH), prostatic intraepithelialneoplasia (PIN), or Atrophy), pimonidazole immunoscore (hypoxia marker),pimonidazole genescore (hypoxia marker), primary tumor (T), regionallymph nodes (N), and distant metastasis (M). For more information aboutthe clinical or pathology parameters in the subset data DB-1, pleaserefer to FIGS. 20M through 20O. Other data or information in the bigdata database 70 may be added to the subset data DB-1. For example, eachof the data sets in the subset data DB-1 may further include riskfactors for cancer associated with the subject, such as smoking history,sun exposure, premalignant lesions, gene information or data, etc. Eachof the data sets in the subset data DB-1 may also include imagingprotocol details, such as MRI magnet strength, and pulse sequenceparameters, and/or information regarding (preoperative) radiationtherapy, including the type of radiation, the strength of radiation, thetotal dose of radiation, the number of fractions (depending on the typeof cancer being treated), the duration of the fraction from start tofinish, the dose of the fraction, the duration of the preoperativeradiation therapy from start to finish, and the type of machine used forthe preoperative radiation therapy. A post-therapy data or informationfor prostate cancer may also be included in the subset data DB-1. Forexample, data regarding ablative minimally invasive techniques orradiation treatments (care for early prostate cancer or post surgery),imaging data or information following treatment, and biopsy resultsfollowing treatment are included in the subset data DB-1.

Referring to FIGS. 1D and 1E, data in the column W of the subset dataDB-1 are various diameters; data in the column X of the subset data DB-1are various lengths; data in the column Y of the subset data DB-1 arethe various numbers of MRI slices registered to respective regions,portions, locations or volumes of a prostate biopsy tissue; data in thecolumn Z of the subset data DB-1 are various MRI area resolutions; datain the column AA of the subset data DB-1 are various MRI slicethicknesses. Alternatively, the diameters of all the prostate biopsytissues in the column W of the subset data DB-1 may be the same; thelengths of all the prostate biopsy tissues in the column X of the subsetdata DB-1 may be the same; all the data in the column Y of the subsetdata DB-1 may be the same; all the data in the column Z of the subsetdata DB-1 may be the same; all the data in the column AA of the subsetdata DB-1 may be the same.

Description of Subset Data DB-2:

Referring to FIGS. 1H-1M, the tissue-based or biopsy-based subset dataDB-2 from the big data database 70 includes multiple data sets eachlisted in the corresponding one of its rows 2 through N, wherein thenumber of the data sets may be greater than 100, 1,000 or 10,000. Eachof the data sets in the subset data DB-2 may include: (1) measures forMRI parameters associated with a breast biopsy tissue (i.e., biopsiedsample of the breast) obtained from a subject (e.g., human or animalmodel), as shown in columns A-O, R, and S; (2) measures for processedparameters associated with the breast biopsy tissue, as shown in columnsP and Q; (3) features of breast tumors associated with the breast biopsytissue, as shown in columns T-Z; (4) a result or pathologist diagnosisof the breast biopsy tissue, such as breast cancer, normal tissue, orbenign condition, as shown in a column AA; (5) sample charactersassociated with the breast biopsy tissue, as shown in columns AB-AD; (6)MRI characters associated with MRI slices registered to respectiveregions, portions, locations or volumes of the breast biopsy tissue, asshown in columns AE-AG; (7) a PET parameter (e.g., maximum standardizeduptake value (SUVmax) depicted in FIG. 20I) associated with the breastbiopsy tissue or the subject, as shown in a column AH; (8) clinical orpathology parameters associated with the breast biopsy tissue or thesubject, as shown in columns AI-AT; and (9) personal informationassociated with the subject, as shown in columns AU-AX. Needles used toobtain the breast biopsy tissues may have the same cross-sectional shape(e.g., round shape or square shape) and the same inner diameter orwidth, e.g., ranging from, equal to or greater than 0.1 millimeters upto, equal to or less than 5 millimeters, and more preferably rangingfrom, equal to or greater than 1 millimeter up to, equal to or less than3 millimeters. Alternatively, an intra-operative incisional biopsytissue sampling may be performed by a surgery to obtain the breastbiopsy. Intraoperative magnetic resonance imaging (iMRI) may be used forobtaining a specific localization of the breast biopsy tissue to bebiopsied during the surgery.

The MRI parameters in the columns A-O, R, and S of the subset data DB-2are T1 mapping, T2 raw signal, T2 mapping, delta Ktrans (A Ktrans), tau,Dt IVIM, fp IVIM, ADC (high b-values), R*, Ktrans from Tofts Model (TM),Ktrans from Extended Tofts Model (ETM), Ktrans from Shutterspeed Model(SSM), Ve from TM, Ve from SSM, kep from Tofts Model (TM), kep fromShutterspeed Model (SSM), and mean diffusivity (MD) from diffusiontensor imaging (DTI). For more information about the MRI parameters inthe subset data DB-2, please refer to FIGS. 20A through 20H. Theprocessed parameter in the column P of the subset data DB-2 is averageVe, obtained by averaging Ve from TM and Ve from SSM. The processedparameter in the column Q of the subset data DB-2 is average Ktrans,obtained by averaging Ktrans from TM, Ktrans from ETM, and Ktrans fromSSM. The features of breast tumors may be extracted from breast tumorswith dynamic contrast-enhanced (DCE) MR image.

Measures in the column AC of the subset data DB-2 may be the diametersof the breast biopsy tissues, and the diameter of each of the breastbiopsy tissues may be substantially equal to an inner diameter of acylinder needle, through which a circular or round hole passes forreceiving said each of the breast biopsy tissues. Alternatively, themeasures in the column AC of the subset data DB-2 may be the widths ofthe breast biopsy tissues, and the width of each of the breast biopsytissues may be substantially equal to an inner width of a needle,through which a square or rectangular hole passes for receiving saideach of the breast biopsy tissues. The clinical or pathology parametersin the columns AI-AT of the subset data DB-2 are estrogen hormonereceptor positive (ER+), progesterone hormone receptor positive (PR+),HER2/neu hormone receptor positive (HER2/neu+), immunohistochemistrysubtype, path, BIRADS, Oncotype DX score, primary tumor (T), regionallymph nodes (N), distant metastasis (M), tumor size, and location. Formore information about the clinical or pathology parameters in thesubset data DB-2, please refer to FIGS. 20P through 20R. Other data orinformation in the big data database 70 may be added to the subset dataDB-2. For example, each of the data sets in the subset data DB-2 mayfurther include specific long chain mRNA biomarkers from next generationsequencing that are predictive of metastasis-free survival, such asHOTAIR, RP11-278 L15.2-001, LINC00511-009, and AC004231.2-001. Each ofthe data sets in the subset data DB-2 may also include risk factors forcancer associated with the subject, such as smoking history, sunexposure, premalignant lesions, gene information or data, etc. Each ofthe data sets in the subset data DB-2 may also include imaging protocoldetails, such as MRI magnet strength, pulse sequence parameters, PETdosing, time at PET imaging, etc.

Referring to FIG. 1K, data in the column AC of the subset data DB-2 arevarious diameters; data in the column AD of the subset data DB-2 arevarious lengths; data in the column AE of the subset data DB-2 are thevarious numbers of MRI slices registered to respective regions,portions, locations or volumes of a breast biopsy tissue; data in thecolumn AF of the subset data DB-2 are various MRI area resolutions; datain the column AG of the subset data DB-2 are various MRI slicethicknesses. Alternatively, the diameters of all the breast biopsytissues in the column AC of the subset data DB-2 may be the same; thelengths of all the breast biopsy tissues in the column AD of the subsetdata DB-2 may be the same; all the data in the column AE of the subsetdata DB-2 may be the same; all the data in the column AF of the dataDB-2 may be the same; all the data in the column AG of the subset dataDB-2 may be the same.

A similar subset data like the subset data DB-1 or DB-2 may beestablished from the big data database 70 for generating probabilitymaps for brain cancer, liver cancer, lung cancer, rectal cancer,sarcomas, cervical cancer, or cancer metastasis to any organ such asliver, bone, and brain. In this case, the subset data may includemultiple data sets, each of which may include: (1) measures for MRIparameters (e.g., those in the columns A-O, R, and S of the subset dataDB-2) associated with a biopsy tissue (e.g., biopsied brain sample,biopsied liver sample, biopsied lung sample, biopsied rectal sample,biopsied sarcomas sample, or biopsied cervix sample) obtained from asubject (e.g., human); (2) processed parameters (e.g., those in thecolumns P and Q of the subset data DB-2) associated with the biopsytissue; (3) a result or pathologist diagnosis of the biopsy tissue, suchas cancer, normal tissue, or benign condition; (4) sample characters(e.g., those in the columns S-X of the subset data DB-1) associated withthe biopsy tissue; (5) MRI characters (e.g., those in the columns Y, Zand AA of the subset data DB-1) associated with MRI slices registered torespective regions, portions, locations or volumes of the biopsy tissue;(6) a PET parameter (e.g., SUVmax depicted in FIG. 20I) associated withthe biopsy tissue or the subject; (7) CT parameters (e.g., HU andHetwave) associated with the biopsy tissue or the subject; (8) clinicalor pathology parameters (e.g., those in the columns AB-AN of the subsetdata DB-1 or the columns AI-AT of the subset data DB-2) associated withthe biopsy tissue or the subject; and (9) personal information (e.g.,those in the columns AO-AR of the subset data DB-1) associated with thesubject.

Description of Biopsy Tissue, MRI Slices Registered to the BiopsyTissue, and MRI Parameters for the Biopsy Tissue:

Referring to FIG. 2A, a biopsy tissue or sample 90, such as any one ofthe biopsied tissues provided for the pathologist diagnosis depicted inthe big data database 70, any one of the prostate biopsy tissuesprovided for the pathologist diagnosis depicted in the subset data DB-1,or any one of the breast biopsy tissues provided for the pathologistdiagnosis depicted in the subset data DB-2, may be obtained from asubject (e.g., human) by core needle biopsy, such as MRI-guided needlebiopsy. Alternatively, an intra-operative incisional biopsy tissuesampling may be performed by a surgery to obtain the biopsy tissue 90from the subject. One or more fiducial markers that could be seen onsubsequent imaging may be placed during the surgery to match tissues oridentify positions of various portions of an organ with respect to theone or more fiducial markers. The fiducial marker is an object placed inthe field of view of an imaging system which appears in the imageproduced, for use as a point of reference or a measure.

The core needle biopsy is a procedure used to determine whether anabnormality or a suspicious area of an organ (e.g., prostate or breast)is a cancer, a normal tissue, or a benign condition or to determine anyother tissue characteristic such as mRNA expression, receptor status,and molecular tissue characteristics. With regard to MRI-guided needlebiopsy, magnetic resonance (MR) imaging may be used to guide a cylinderneedle to the abnormality or the suspicious area so that a piece oftissue, such as the biopsy tissue 90, is removed from the abnormality orthe suspicious area by the cylinder needle, and the removed tissue isthen sent to be examined by pathology.

During or before the core needle biopsy (e.g., MRI-guided needlebiopsy), parallel MRI slices SI₁ through SI_(N) registered to multiplerespective regions, portions, locations or volumes of the tissue 90 maybe obtained. The number of the registered MRI slices SI₁-SI_(N) mayrange from, equal to or greater than 2 up to, equal to or less than 10.The registered MRI slices SI₁-SI_(N) may have the same slice thicknessT, e.g., ranging from, equal to or greater than 1 millimeter up to,equal to or less than 10 millimeters, and more preferably ranging from,equal to or greater than 3 millimeters up to, equal to or less than 5millimeters.

Referring to FIGS. 2A and 2E, the biopsy tissue 90 obtained from thesubject by the cylinder needle may be long cylinder-shaped with a heighttT normalized to the slice thickness T and with a circular cross sectionperpendicular to its axial direction AD, and the circular cross sectionof the biopsy tissue 90 may have a diameter D1, perpendicular to itsheight tT extending along the axial direction AD, ranging from, equal toor greater than 0.5 millimeters up to, equal to or less than 4millimeters. The diameter D1 of the biopsy tissue 90 may besubstantially equal to an inner diameter of the cylinder needle, throughwhich a circular or round hole passes for receiving the biopsy tissue90. The axial direction AD of the tissue 90 to be biopsied may beparallel with the slice thickness direction of each of the MRI slicesSI₁-SI_(N). As shown in FIG. 2B, each of the MRI slices SI₁-SI_(N) mayhave an imaging plane 92 perpendicular to the axial direction AD of thetissue 90 to be biopsied, wherein an area of the imaging plane 92 is aside length W1 multiplied by another side length W2. The MRI slicesSI₁-SI_(N) may have the same area resolution, which is a field of view(FOV) of one of the MRI slices SI₁-SI_(N) (i.e., the area of its imagingplane 92) divided by the number of all voxels in the imaging plane 92 ofsaid one of the MRI slices SI₁-SI_(N).

Regions, i.e., portions, locations or volumes, of interest (ROIs) 94 ofthe respective MRI slices SI₁-SI_(N) arc registered to and aligned withthe respective regions, portions, locations or volumes of the biopsytissue 90 to determine or calculate measures of MRI parameters for theregions, portions, locations or volumes of the biopsy tissue 90. TheROIs 94 of the MRI slices SI₁-SI_(N) may have the same diameter,substantially equal to the diameter D1 of the biopsy tissue 90, i.e.,the inner diameter of the needle for taking the biopsy tissue 90, andmay have a total volume covering and substantially equaling the volumeof the biopsy tissue 90. As shown in FIG. 2C, the ROI 94 of each of theMRI slices M₁-SI_(N) may cover or overlap multiple voxels, e.g., 96 athrough 96 f. A MRI parameter (e.g., T1 mapping) for the ROI 94 of eachof the MRI slices SI₁-SI_(N) may be measured by summing values of theMRI parameter for the voxels 96 a-96 f in said each of the MRI slicesSI₁-SI_(N) weighed or multiplied by the respective percentages of areasA1, A2, A3, A4, A5 and A6, overlapping with the respective voxels 96a-96 f in the ROI 94 of said each of the MRI slices SI₁-SI_(N),occupying the ROI 94 of said each of the MRI slices Accordingly, the MRIparameter for the whole biopsy tissue 90 may be measured by dividing thesum of measures for the MRI parameter for the ROIs 94 of the MRI slicesSI₁-SI_(N) by the number of the MRI slices SI₁-SI_(N). By this way,other MRI parameters (e.g., those in the columns B-O of the subset dataDB-1 or those in the columns B-O, R and S of the subset data DB-2) forthe whole biopsy tissue 90 are measured. The measures for the variousMRI parameters (e.g., T1 mapping, T2 raw signal, T2 mapping, etc.) forthe ROI 94 of each of the MRI slices SI₁-SI_(N) may be derived fromdifferent parameter maps registered to the corresponding region,portion, location or volume of the biopsy tissue 90. In an alternativeexample, the measures for some of the MRI parameters for the ROI 94 ofeach of the MRI slices SI₁-SI_(N) may be derived from differentparameter maps registered to the corresponding region, portion, locationor volume of the biopsy tissue 90, and the measures for the others maybe derived from the same parameter map registered to the correspondingregion, portion, location or volume of the biopsy tissue 90. Theaforementioned method for measuring the MRI parameters for the wholebiopsy tissue 90 can be applied to each of the MRI parameters in the bigdata database 70 and the subset data DB-1 and DB-2.

Taking an example of T1 mapping, in the case of (1) four MRI slicesSI₁-SI₄ having four respective regions, portions, locations or volumesregistered to respective quarters of the biopsy tissue 90 and (2) theROI 94 of each of the MRI slices SI₁-SI₄ covering or overlapping the sixvoxels 96 a-96 f, values of T1 mapping for the voxels 96 a-96 f in eachof the MRI slices SI₁-SI₄ and the percentages of the areas A1-A6occupying the ROI 94 of each of the MRI slices SI₁-SI₄ are assumed asshown in FIG. 2D. A measure of T1 mapping for the ROI 94 of the MRIslice SI₁, i.e., 1010.64, may be obtained or calculated by summing (1)the value, i.e., 1010, for the voxel 96 a multiplied by the percentage,i.e., 6%, of the area A1, overlapping with the voxel 96 a in the ROI 94of the MRI slice SI₁, occupying the ROI 94 of the MRI slice SI₁, (2) thevalue, i.e., 1000, for the voxel 96 b multiplied by the percentage,i.e., 38%, of the area A2, overlapping with the voxel 96 b in the ROI 94of the MRI slice SI₁, occupying the ROI 94 of the MRI slice SI₁, (3) thevalue, i.e., 1005, for the voxel 96 c multiplied by the percentage,i.e., 6%, of the area A3, overlapping with the voxel 96 c in the ROI 94of the MRI slice SI₁, occupying the ROI 94 of the MRI slice SI₁, (4) thevalue, i.e., 1020, for the voxel 96 d multiplied by the percentage,i.e., 6%, of the area A4, overlapping with the voxel 96 d in the ROI 94of the MRI slice SI₁, occupying the ROI 94 of the MRI slice SI₁, (5) thevalue, i.e., 1019, for the voxel 96 e multiplied by the percentage,i.e., 38%, of the area A5, overlapping with the voxel 96 e in the ROI 94of the MRI slice SI₁, occupying the ROI 94 of the MRI slice SI₁, and (6)the value, i.e., 1022, for the voxel 96 f multiplied by the percentage,i.e., 6%, of the area A6, overlapping with the voxel 96 f in the ROI 94of the MRI slice SI₁, occupying the ROI 94 of the MRI slice SI₁. By thisway, T1 mapping for the ROIs 94 of the MRI slices SI₂, SI₃, and SI₄,i.e., 1006.94, 1022, and 1015.4, are obtained or measured. Accordingly,T1 mapping for the whole biopsy tissue 90, i.e., 1013.745, is obtainedor measured by dividing the sum, i.e., 4054.98, of T1 mapping for theROIs 94 of the MRI slices SI₁-SI₄ by the number of the MRI slicesSI₁-SI₄, i.e., 4.

The volume of the long cylinder-shaped biopsied tissue 90 may betransformed into another shape, which may have a volume the same orabout the same as the volume of the long cylinder-shaped biopsied tissue90 (or Volume of Interest (VOI), which may be π×Rn²×tT, where Rn is theradius of the biopsied tissue 90, and tT is the height of the biopsiedtissue 90), for easy or meaningful computing purposes, for medicalinstrumentation purposes, or for clearer final data presentationpurposes. For example, referring to FIG. 2E, the long cylinder of thebiopsied tissue 90 with the radius Rn and height tT may be transformedinto a planar cylinder 98 to match the slice thickness T. The planarcylinder 98, having a volume, e.g., the same or about the same as theVOI of the biopsied tissue 90, may be defined by the following formula:π×Rn²×M×St=π×Rw²×pT, where Rn is the radius of the biopsy tissue 90(which is substantially equal to the inner radius of the needle fortaking the biopsy tissue 90), M is the number of the MRI slicesSI₁-SI_(N), St is the slice thickness T of the MRI slices SI₁-SI_(N), Rwis the radius of the planar cylinder 98, and pT is the height orthickness of the planar cylinder 98 perpendicular to the radius Rw ofthe planar cylinder 98. The height tT of the biopsy tissue 90 may besubstantially equal to the slice thickness T multiplied by the number ofthe MRI slices SI₁-SI_(N). In the invention, the height pT of the planarcylinder 98 is substantially equal to the slice thickness T, forexample. Accordingly, the planar cylinder 98 may have the height pTequal to the slice thickness T and the radius Rw equal to the radius Rnmultiplied by the square root of the number of the registered MRI slicesSI₁-SI_(N). The radius Rw of the planner cylinder 98 may be used todefine the radius Rm of a moving window MW in calculating probabilitymaps, e.g., illustrated in first through sixth embodiments, for apatient (e.g., human). Each of the biopsy tissue 90, the planar cylinder98 and the moving window MW may have a volume at least 2, 3, 5, 10 or 15times greater than that of each voxel of the MRI slices SI₁-SI_(N) andthan that of each voxel of an MRI image 10 from a subject (e.g.,patient) depicted in a step S1 of FIG. 4. In addition, because theplanar cylinder 98 is transformed from the biopsy tissue 90, themeasures of the MRI parameters for the whole biopsy tissue 90 may beconsidered as those for the planar cylinder 98.

Further, each of biopsy tissues provided for pathologist diagnoses in asubset data, e.g., DB-1 or DB-2, of the big data database 70 may have acorresponding planar cylinder 98 with its radius Rw, and data (such aspathologist diagnosis and measures of imaging parameters) for said eachof the biopsy tissues in the subset data, e.g., DB-1 or DB-2, of the bigdata database 70 may be considered as those for the corresponding planarcylinder 98. Statistically, the moving window MW may be determined withthe radius Rm, perpendicular to a thickness of the moving window MW,based on the statistical distribution or average of the radii Rw of theplanar cylinders 98 transformed from the volumes of the biopsy tissuesprovided for the pathologist diagnoses in the subset data, e.g., DB-1 orDB-2, of the big data database 70. In the invention, each of the biopsytissues provided for the pathologist diagnoses in the subset data, e.g.,DB-1 or DB-2, of the big data database 70, for example, may have avolume, i.e., VOI, substantially equal to the volume of the movingwindow MW to be used in calculating one or more probability maps. Inother words, the volume of the biopsy tissue, i.e., VOI, defines thesize (e.g., the radius Rm) of the moving window MW to be used incalculating one or more probability maps.

Each of the prostate biopsy tissues provided for the pathologistdiagnoses in the subset data DB-1 may be referred to the illustration ofthe biopsy tissue 90. In the column W of the subset data. DB-1, thediameter of each of the prostate biopsy tissues may be referred to theillustration of the diameter D1 of the biopsy tissue 90. The MRI slicesregistered to the respective regions, portions, locations or volumes ofeach of the prostate biopsy tissues provided for the pathologistdiagnoses in the subset data DB-1 may be referred to the illustration ofthe MRI slices SI₁-SI_(N) registered to the respective regions,portions, locations or volumes of the biopsy tissue 90. The measures ofthe MRI parameters for each of the prostate biopsy tissues, i.e., foreach of the corresponding planar cylinders 98, in the respective columnsA-O of the subset data DB-1 may be calculated as the measures of the MRIparameters for the whole biopsy tissue 90, i.e., for the planar cylinder98 transformed from the volume of the biopsy tissue 90, are calculated.In the column Z of the subset data DB-1, the MRI slices registered tothe respective regions, portions, locations or volumes of each of theprostate biopsy tissues may have the same area resolution, which may bereferred to the illustration of the area resolution of the MRI slicesSI_(T)-SI_(N) registered to the respective regions, portions, locationsor volumes of the biopsy tissue 90. In the column AA of the subset dataDB-1, the MRI slices registered to the respective regions, portions,locations or volumes of each of the prostate biopsy tissues may have thesame slice thickness, which may be referred to the illustration of theslice thickness T of the MRI slices SI_(T)-SI_(N).

In the column S of the subset data DB-1, the percentage of cancer forthe whole volume of the prostate biopsy tissue in each of all or some ofthe data sets may be replaced by the percentage of cancer for a partialvolume of the prostate biopsy tissue; a MRI slice is imaged for andregistered to the partial volume of the prostate biopsy tissue. In thiscase, the MRI parameters, in the columns A-O of the subset data DB-1,that are in said each of all or some of the data sets are measured for aROI of the MRI slice registered to the partial volume of the prostatebiopsy tissue. The ROI of the MRI slice covers or overlaps multiplevoxels in the MRI slice, and each of the MM parameters for the ROI ofthe MRI slice may be measured by summing values of said each of the MRIparameters for the voxels weighed or multiplied by respectivepercentages of areas, overlapping with the respective voxels in the ROIof the MRI slice, occupying the ROI of the MRI slice. Measures for theMM parameters for the ROI of the MRI slice may be derived from differentparameter maps registered to the partial volume of the prostate biopsytissue. In an alternative example, the measures for some of the MRIparameters for the ROI of the MRI slice may be derived from differentparameter maps registered to the partial volume of the prostate biopsytissue, and the measures for the others may be derived from the sameparameter map registered to the partial volume of the prostate biopsytissue.

Each of the breast biopsy tissues provided for the pathologist diagnosesin the subset data DB-2 may be referred to the illustration of thebiopsy tissue 90. In the column AC of the subset data DB-2, the diameterof each of the breast biopsy tissues may be referred to the illustrationof the diameter D1 of the biopsy tissue 90. The MRI slices registered tothe respective regions, portions, locations or volumes of each of thebreast biopsy tissues provided for the pathologist diagnoses in thesubset data DB-2 may be referred to the illustration of the MRI slicesSI₁-SI_(N) registered to the respective regions, portions, locations orvolumes of the biopsy tissue 90. The measures of the MRI parameters foreach of the breast biopsy tissues, i.e., for each of the correspondingplanar cylinders 98, in the respective columns A-O, R, and S of thesubset data DB-2 may be calculated as the measures of the MRI parametersfor the whole biopsy tissue 90, i.e., for the planar cylinder 98transformed from the volume of the biopsy tissue 90, are calculated. Inthe column AF of the subset data DB-2, the MRI slices registered to therespective regions, portions, locations or volumes of each of the breastbiopsy tissues may have the same area resolution, which may be referredto the illustration of the area resolution of the MRI slices SI₁-SI_(N)registered to the respective regions, portions, locations or volumes ofthe biopsy tissue 90. In the column AG of the subset data DB-2, the MRIslices registered to the respective regions, portions, locations orvolumes of each of the breast biopsy tissues may have the same slicethickness, which may be referred to the illustration of the slicethickness T of the MRI slices SI₁-SI_(N).

In the column AB of the subset data DB-2, the percentage of cancer forthe whole volume of the breast biopsy tissue in each of all or some ofthe data sets may be replaced by the percentage of cancer for a partialvolume of the breast biopsy tissue; a MRI slice is imaged for andregistered to the partial volume of the breast biopsy tissue. In thiscase, the MRI parameters, in the columns A-O, R, and S of the subsetdata DB-2, that are in said each of all or some of the data sets aremeasured for a ROI of the MRI slice registered to the partial volume ofthe breast biopsy tissue. The ROI of the MRI slice covers or overlapsmultiple voxels in the MRI slice, and each of the MRI parameters for theROI of the MRI slice may be measured by summing values of said each ofthe MRI parameters for the voxels weighed or multiplied by respectivepercentages of areas, overlapping with the respective voxels in the ROIof the MRI slice, occupying the ROI of the MRI slice. Measures for theMRI parameters for the ROI of the MRI slice may be derived fromdifferent parameter maps registered to the partial volume of the breastbiopsy tissue. In an alternative example, the measures for some of theMRI parameters for the ROI of the MRI slice may be derived fromdifferent parameter maps registered to the partial volume of the breastbiopsy tissue, and the measures for the others may be derived from thesame parameter map registered to the partial volume of the breast biopsytissue.

In an alternative example, the biopsied tissue 90 may be obtained by aneedle with a square through hole therein. In this case, the biopsiedtissue 90 may have a longitudinal shape with a square-shapedcross-section having a width Wb (which is substantially equal to aninner width of the needle, i.e., the width of the square through hole ofthe needle) and a height Ht (which is substantially equal to, e.g., theslice thickness T multiplied by the number of the MRI slicesSI₁-SI_(N)). The volume of the biopsied tissue 90 may be transformedinto a flat square FS with a width Wf and a thickness or height fT. Theflat square FS, having a volume the same or about the same as the volumeof the biopsied tissue 90 (or Volume of Interest (VOI), which may be theheight Ht multiplied by the square of the width Wb), may be defined bythe following formula: Wb²×M×St=Wf²×fT, where Wb is the width of thebiopsy tissue 90, M is the number of the MRI slices SI₁-SI_(N), St isthe slice thickness T of the MRI slices SI₁-SI_(N), Wf is the width ofthe flat square FS, and fT is the height or thickness of the flat squareFS perpendicular to the width Wf of the flat square FS. In theinvention, the height or thickness fT of the flat square FS issubstantially equal to the slice thickness T, for example. Accordingly,the flat square FS may have the height or thickness fT equal to theslice thickness T and the width Wf equal to the width Wb multiplied bythe square root of the number of the registered MRI slices SI₁-SI_(N).In the case of the moving window MW with a square shape, the width Wf ofthe flat square FS may be used to define the width of the moving windowMW in calculating probability maps. Each of the biopsy tissue 90, theflat square FS and the square moving window MW may have a volume atleast 2, 3, 5, 10 or 15 times greater than that of each voxel of the MRIslices SI₁-SI_(N) and than that of each voxel of an MRI image, e.g., 10from a subject (e.g., patient) depicted in a step S1 of FIG. 4. Further,each of biopsy tissues provided for pathologist diagnoses in a subsetdata of the big data database 70 may have a corresponding flat square FSwith its width Wf, and data (such as pathologist diagnosis and measuresof imaging parameters) for said each of the biopsy tissues in the subsetdata of the big data database 70 may be considered as those for thecorresponding flat square FS.

Description of Area Resolution and Voxels of a Single MRI Slice:

In the invention, an area resolution of a single MRI slice such assingle slice MRI image 10 shown in FIG. 5 or 19 is a field of view (FOV)of the single MRI slice divided by the number of all voxels in the FOVof the single MRI slice. Each of the voxels of the single MRI slice mayhave a pixel (or pixel plane), perpendicular to the slice thicknessdirection of the single MRI slice, having a square area with the samefour side lengths.

Description of Moving Window and Probability Map:

Any probability map in the invention may be composed of multiplecomputation voxels with the same size, which are basic units of theprobability map. The size of the computation voxels used to compose theprobability map may be defined based on the size of the moving windowMW, which is determined or defined based on information data associatedwith the biopsy tissues provided for the pathologist diagnoses in thesubset data, e.g., DB-1 or DB-2, of the big data database 70. Theinformation data, for example, may include the radii Rw of planarcylinders 98 transformed from the volumes of the biopsy tissues. Inaddition, each of the computation voxels of the probability map may havea volume or size equal to, greater than or less than that of any voxelin a single MRI slice, such as MRI image 10 shown in FIG. 5 or 19,depicted in steps S1-S6 of FIG. 4.

The moving window MW may have various shapes, such as a circular shape,a square shape, a rectangular shape, a hexagonal shape, or an octagonalshape. In the invention, referring to FIG. 3A, the moving window MW is acircular moving window 2 with a radius Rm, for example. The radius Rm ofthe circular moving window 2 may be calculated, determined, or definedbased on the statistical distribution or average of the radii Rw ofplanar cylinders 98 obtained from biopsy tissues associated with asubset data, e.g., DB-1 or DB-2, of the big data database 70. Forexample, in the first embodiment of the invention, the radius Rm of thecircular moving window 2 may be calculated, determined or defined basedon the statistical distribution or average of the radii Rw of the planarcylinders 98 obtained from the prostate biopsy tissues associated withthe subset data DB-1; the approach to obtain the radius Rw of the planarcylinder 98 from the biopsy tissue 90 may be applied to obtain the radiiRw of the planar cylinders 98 from the prostate biopsy tissuesassociated with the subset data DB-1. In the third embodiment of theinvention, the radius Rm of the circular moving window 2 may becalculated, determined or defined based on the statistical distributionor average of the radii Rw of the planar cylinders 98 obtained from thebreast biopsy tissues associated with the subset data DB-2; the approachto obtain the radius Rw of the planar cylinder 98 from the biopsy tissue90 may be applied to obtain the radii Rw of the planar cylinders 98 fromthe breast biopsy tissues associated with the subset data DB-2.

Referring to FIG. 3A, 3B or 3C, a square 4 having its four verticeslying on the circular moving window 2, i.e., the biggest square 4inscribed in the circular moving window 2, is defined and divided intomultiple small units or grids 6. The small grids 6 may be n² smallsquares each having a width Wsq, where n is an integer, such as 2, 3, 4,5, 6, or more than 6. Based on the size (e.g., the width Wsq) and shapeof the divided squares 6, the size and shape of the computation voxelsused to compose the probability map may be defined. In other words, eachof the computation voxels used to compose the probability map, forexample, may be defined as a square with the width Wsq and a volume thesame or about the same as that of each square 6 based on the radius Rmof the circular moving window 2 and the number of the squares 6 in thecircular moving window 2, i.e., based on the width Wsq of the squares 6in the circular moving window 2.

The circular moving window 2 in FIG. 3A is shown with a two-by-twosquare array in the square 4, each square 6 of which has the same area(i.e., a quarter of the square 4). In FIG. 3A, the four non-overlappedsquares 6 have the same width Wsq, which is equal to the radius Rm ofthe circular moving window 2 divided by √{square root over (2)}. In thecase of the circular moving window 2 having the radius Rm of √{squareroot over (2)} millimeters, each square 6 may have an area of 1millimeter by 1 millimeter, that is, each square 6 has the width Wsq of1 millimeter.

In an alternative example, referring to FIG. 3B, the square 4 may have athree-by-three square array, each square 6 of which has the same area(i.e., a ninth of the square 4); the nine non-overlapped squares 6 havethe same width Wsq, which is equal to the radius Rm of the circularmoving window 2 divided by ⅔√{square root over (2)}. In an alternativeexample, referring to FIG. 3C, the square 4 may have a four-by-foursquare array, each square 6 of which has the same area (i.e., onesixteenth of the square 4); the sixteen non-overlapped squares 6 havethe same width Wsq, which is equal to the radius Rm of the circularmoving window 2 divided by 2√{square root over (2)}.

Accordingly, the moving window MW (e.g., the circular moving window 2)may be defined to include four or more non-overlapped grids 6 having thesame square shape, the same size or area (e.g., 1 millimeter by 1millimeter), and the same width Wsq, e.g., equal to, greater than orless than any side length of pixels of voxels in a single MRI slice,such as MRI image 10 shown in FIG. 5 or 19, depicted in the steps S1-S3of FIG. 4. Each of the squares 6, for example, may have an area lessthan 25% of that of the moving window MW and equal to, greater than orless than that of the pixel of each voxel of the single MRI slice; eachof the squares 6, for example, may have a volume equal to, greater thanor less than that of each voxel of the single MRI slice. In the case ofthe moving window MW defined to include four or more non-overlappedsquares 6 with the width Wsq, the moving window MW may move across thesingle MRI slice at a regular step or interval of a fixed distance ofthe width Wsq in the x and y directions so that the computation voxelsof the probability map are defined. A stop of the moving window MWoverlaps the neighboring stop of the moving window MW.

Alternatively, the grids 6 may be n rectangles each having a width Wrecand a length Lrec, where n is an integer, such as 2, 3, 4, 5, 6, 7, 8,or more than 8. Based on the size (e.g., the width Wrec and the lengthLrec) and shape of the divided rectangles 6, the size and shape of thecomputation voxels used to compose the probability map may be defined.In other words, each of the computation voxels used to compose theprobability map, for example, may be defined as a rectangle with thewidth Wrec, the length Lrec, and a volume the same or about the same asthat of each rectangle 6 based on the radius Rm of the circular movingwindow 2 and the number of the rectangles 6 in the circular movingwindow 2, i.e., based on the width Wrec and length Lrec of therectangles 6 in the circular moving window 2. Accordingly, the movingwindow MW (e.g., the circular moving window 2) may be defined to includefour or more non-overlapped grids 6 having the same rectangle shape, thesame size or area, the same width Wrec, e.g., equal to, greater than orless than any side length of pixels of voxels in a single MRI slice,such as MRI image 10 shown in FIG. 5 or 19, depicted in the steps S1-S3of FIG. 4, and the same length Lrec, e.g., equal to, greater than orless than any side length of the pixels of the voxels in the single MRIslice. Each of the rectangles 6, for example, may have an area less than25% of that of the moving window MW and equal to, greater than or lessthan that of the pixel of each voxel of the single MRI slice. Each ofthe rectangles 6, for example, may have a volume equal to, greater thanor less than that of each voxel of the single MRI slice. In the case ofthe moving window MW defined to include four or more non-overlappedrectangles 6 with the width Wrec and the length Lrec, the moving windowMW may move across the single MRI slice at a regular step or interval ofa fixed distance of the width Wrec in the x direction and at a regularstep or interval of a fixed distance of the length Lrec in the ydirection so that the computation voxels of the probability map aredefined. A stop of the moving window MW overlaps the neighboring stop ofthe moving window MW.

In the case of the moving window MW with a square shape, the squaremoving window MW may be determined with a width Wsm based on thestatistical distribution or average of the widths Wf of flat squares FSobtained from biopsy tissues associated with a subset data of the bigdata database 70. The square moving window MW may be divided into theaforementioned small grids 6. In this case, each of the computationvoxels of the probability map, for example, may be defined as a squarewith the width Wsq and a volume the same or about the same as that ofeach square 6 based on the width Wsm of the square moving window MW andthe number of the squares 6 in the square moving window MW, i.e., basedon the width Wsq of the squares 6 in the square moving window MW.Alternatively, each of the computation voxels of the probability map maybe defined as a rectangle with the width Wrec, the length Lrec, and avolume the same or about the same as that of each rectangle 6 based onthe width Wsm of the square moving window MW and the number of therectangles 6 in the square moving window MW, i.e., based on the widthWrec and length Lrec of the rectangles 6 in the square moving window MW.

Description of Classifier CF:

The classifier CF for an event, such as biopsy-diagnosed tissue or tumorcharacteristic for, e.g., specific cancerous cells or occurrence ofprostate cancer or breast cancer, may be created or established based ona subset (e.g., the subset data DB-1 or DB-2 or the aforementionedsubset data established for generating the voxelwise probability map ofbrain cancer, liver cancer, lung cancer, rectal cancer, sarcomas,cervical cancer, or cancer metastasis to any organ such as liver, bone,and brain) obtained from the big data database 70. The subset may haveall data associated with the given event from the big data database 70.The classifier CF may be a Bayesian classifier, which may be created byperforming the following steps: constructing database, preprocessingparameters, ranking parameters, identifying a training dataset, anddetermining posterior probabilities for test data.

In the step of constructing database, a first group and a second groupmay be determined or selected from a tissue-based or biopsy-based subsetdata, such as the aforementioned subset data, e.g., DB-1 or DB-2, fromthe big data database 70, and various variables associated with each ofthe first and second groups are obtained from the tissue-based orbiopsy-based subset data. The variables may be MRI parameters in thecolumns A-O of the subset data DB-1 or the columns A-O, R, and S of thesubset data DB-2. Alternatively, the variables may be T1 mapping, T2 rawsignal, T2 mapping, delta Ktrans, tau, Dt IVIM, fp IVIM, ADC (highb-values), R*, Ktrans from TM, Ktrans from ETM, Ktrans from SSM, Ve fromTM, Ve from ETM, Ve from SSM, and standard PET.

The first group, for example, may be associated with a first data typeor feature in a specific column of the subset data DB-1 or DB-2, and thesecond group may be associated with a second data type or feature in thespecific column of the subset data DB-1 or DB-2, wherein the specificcolumn of the subset data DB-1 or DB-2 may be one of the columns R-AR ofthe subset data DB-1 or one of the columns AA-AX of the subset dataDB-2. In a first example, the first data type is associated withprostate cancer in the column R of the subset data DB-1, and the seconddata type is associated with non-prostate cancer (e.g., normal tissueand benign condition) in the column R of the subset data DB-1. In asecond example, the first data type is associated with breast cancer inthe column AA of the subset data DB-2, and the second data type isassociated with non-breast cancer (e.g., normal tissue and benigncondition) in the column AA of the subset data DB-2. In the case of thefirst group associated with a cancer type (e.g., prostate cancer orbreast cancer) and the second group associated with a non-cancer type(e.g., non-prostate cancer or non-breast cancer), the cancer type mayinclude data of interest for a single parameter, such as malignancy,mRNA expression, etc., and the non-cancer type may include normal tissueand benign conditions. The benign conditions may vary based on tissues.For example, the benign conditions for breast tissues may includefibroadenomas, cysts, etc.

In a third example, the first data type is associated with one ofGleason scores 0 through 10, such as Gleason score 5, in the column T ofthe subset data DB-1, and the second data type is associated with theothers of Gleason scores 0 through 10, such as Gleason scores 0 through4 and 6 through 10, in the column T of the subset data DB-1. In a fourthexample, the first data type is associated with two or more of Gleasonscores 0 through 10, such as Gleason scores greater than 7, in thecolumn T of the subset data DB-1, and the second data type is associatedwith the others of Gleason scores 0 through 10, such as Gleason scoresequal to and less than 7, in the column T of the subset data DB-1. In afifth example, the first data type is associated with the percentage ofcancer in a specific range from a first percent (e.g., 91 percent) to asecond percent (e.g., 100 percent) in the column S of the subset dataDB-1, and the second data type is associated with the percentage ofcancer beyond the specific range in the column S of the subset dataDB-1. In a sixth example, the first data type is associated with a smallcell subtype in the column AE of the subset data DB-1, and the seconddata type is associated with a non-small cell subtype in the column AEof the subset data DB-1. Any event depicted in the invention may be theabove-mentioned first data type or feature, occurrence of prostatecancer, occurrence of breast cancer, or a biopsy-diagnosed tissue ortumor characteristic for, e.g., specific cancerous cells.

After the step of constructing database is completed, the step ofpreprocessing parameters is performed to determine what the variablesare conditionally independent. A technique for dimensionality reductionmay allow reduction of some of the variables that are conditionallydependent to a single variable. Use of dimensionality reductionpreprocessing of data may allow optimal use of all valuable informationin datasets. The simplest method for dimensionality reduction may besimple aggregation and averaging of datasets. In one example,aggregation may be used for dynamic contrast-enhanced MRI (DCE-MRI)datasets. Ktrans and Ve measures from various different pharmacokineticmodeling techniques may be averaged to reduce errors and optimizesensitivity to tissue change.

For the variables, averaging and subtraction may be used to consolidatemeasures. Accordingly, five or more types of parameters may be selectedor obtained from the variables. The five or more selected parameters areconditionally independent and may include T1 mapping, T2 mapping, deltaKtrans (obtained by subtracting “Ktrans from Tofts Model” from “Ktransfrom Shutterspeed Model”), tau, Dt IVIM, fp IVIM, R*, average Ve, andaverage Ktrans in the respective columns A, C-G, J, P, and Q of thesubset data DB-1 or DB-2. Alternatively, the five or more selectedparameters may include T1 mapping, T2 mapping, delta Ktrans, tau, fpIVIM, R*, average Ve, average Ktrans, standard PET, and a parameter Dobtained by averaging Dt IVIM and ADC (high b-values), wherein theparameter D is conditionally independent of every other selectedparameter.

After the step of preprocessing parameters is complete, the step ofranking parameters is performed to determine the optimal ones of thefive or more selected parameters for use in classification, e.g., tofind the optimal parameters that are most likely to give the highestposterior probabilities, so that a rank list of the five or moreselected parameters is obtained. A filtering method, such as t-test, maybe to look for an optimal distance between the first group (indicated byGR1) and the second group (indicated by GR2) for every one of the fiveor more selected parameters, as shown in FIG. 24. FIG. 24 shows twoGaussian curves of two given different groups (i.e., the first andsecond groups GR1 and GR2) with respect to parameter measures. In FIG.24, X axis is values for a specific parameter, and Y axis is the numberof tissue biopsies.

Four different criteria may be computed for ranking the five or moreselected parameters. The first criterion is the p-value derived from at-test of the hypothesis that the two features sets, corresponding tothe first group and the second group, coming from distributions withequal means. The second criterion is the mutual information (MI)computed between the classes and each of the first and second groups.The last two criteria are derived from the minimum redundancymaximumrelevance (mRMR) selection method.

In the step of identifying a training dataset, a training dataset of thefirst group and the second group is identified based on the rank listafter the step of ranking parameters, and thereby the Bayesianclassifier may be created based on the training dataset of the firstgroup and the second group. In the step of determining posteriorprobabilities for test data, the posterior probabilities for the testdata may be determined using the Bayesian classifier. Once the Bayesianclassifier is created, the test data may be applied to predict posteriorprobabilities for high resolution probability maps.

In an alternative example, the classifier CF may be a neural network(e.g., probabilistic neural network, single-layer feed forward neuralnetwork, multi-layer perception neural network, or radial basis functionneural network), a discriminant analysis, a decision tree (e.g.,classification and regression tree, quick unbiased and efficientstatistical tree, Chi-square automatic interaction detector, C5.0, orrandom forest decision tree), an adaptive boosting, a K-nearestneighbors algorithm, or a support vector machine. In this case, theclassifier CF may be created based on information associated with thevarious MRI parameters for the ROIs 94 of the MRI slices SI₁-SI_(N)registered to each of the biopsy tissues depicted in the subset data.DB-1 or DB-2.

First Embodiment:

After the big data database 70 and the classifier CF are created orconstructed, a (voxelwise) probability map (i.e., a decision data map),composed of multiple computation voxels with the same size, for an event(i.e., a decision-making characteristic) may be generated or constructedfor, e.g., evaluating or deteimining the health status of a subject suchas healthy individual or patient, the physical condition of an organ orother structure inside the subject's body, or the subject's progress andtherapeutic effectiveness by sequentially performing six steps S1through S6 illustrated in FIG. 4. The steps S1-S6 may be performed basedon the moving window MW with a suitable shape such as a circular shape,a square shape, a rectangular shape, a hexagonal shape, or an octagonalshape. The moving window MW is selected for a circular shape, i.e., thecircular moving window 2, to perform the steps S1-S6 as mentioned in thefollowing paragraphs. Referring to FIG. 4, in the step S1, a MRI image10 (single slice) shown in FIG. 5 is obtained from the subject by a MRIdevice or system. The MRI image 10 (i.e., a molecular image) is composedof multiple voxels in its field of view (FOV) to show an anatomicalregion of the subject, such as prostate. In an alternative embodiment,the MRI image 10 may show another anatomical region of the subject, suchas breast, brain, liver, lung, cervix, bone, sarcomas, metastatic lesionor site, capsule around the prostate, pelvic lymph nodes around theprostate, or lymph node.

In the step S2, a desired or anticipated region 11 is determined on theMRI image 10, and a computation region 12 for the probability map is setin the desired or anticipated region 11 of the MRI image 10 and definedwith the computation voxels based on the size (e.g., the radius Rm) ofthe moving window 2 and the size and shape of the small grids 6 in themoving window 2 such as the width Wsq of the small squares 6 or thewidth Wrec and the length Lrec of the small rectangles 6. A side lengthof the computation region 12 in the x direction, for example, may becalculated by obtaining a first maximum positive integer of a sidelength of the desired or anticipated region 11 in the x directiondivided by the width Wsq of the small squares 6 in the moving window 2,and multiplying the width Wsq by the first maximum positive integer aside length of the computation region 12 in the y direction may becalculated by obtaining a second maximum positive integer of a sidelength of the desired or anticipated region 11 in the y directiondivided by the width Wsq of the small squares 6 in the moving window 2,and multiplying the width Wsq by the second maximum positive integer.Alternatively, a side length of the computation region 12 in the xdirection may be calculated by obtaining a first maximum positiveinteger of a side length of the desired or anticipated region 11 in thex direction divided by the width Wrec of the small rectangles 6 in themoving window 2, and multiplying the width Wrec by the first maximumpositive integer; a side length of the computation region 12 in the ydirection may be calculated by obtaining a second maximum positiveinteger of a side length of the desired or anticipated region 11 in they direction divided by the length Lrec of the small rectangles 6 in themoving window 2, and multiplying the length Lrec by the second maximumpositive integer. The computation region 12 may cover at least 10, 25,50, 80, 90 or 95 percent of the FOV of the MRI image 10, which mayinclude the anatomical region of the subject. The computation region 12,for example, may be shaped like a parallelogram such as square orrectangle.

The size and shape of the computation voxels used to compose theprobability map, for example, may be defined based on the radius Rm ofthe moving window 2, wherein the radius Rm is calculated based on, e.g.,the statistical distribution or average of the radii Rw of the planarcylinders 98 transformed from the volumes of the prostate biopsy tissuesprovided for the pathologist diagnoses depicted in the subset data DB-1,as illustrated in the section of “description of moving window andprobability map”. Each of the computation voxels, for example, may bedefined as a square with the width Wsq in the case of the moving window2 defined to include the small squares 6 each having the width Wsq.Alternatively, each of the computation voxels may be defined as arectangle with the width Wrec and the length Lrec in the case of themoving window 2 defined to include the small rectangles 6 each havingthe width Wrec and the length Lrec.

A step for abbreviated search functions (such as looking for one or morespecific areas of the MRI image 10 where diffusion signals arc above acertain signal value) may be performed between the steps S1 and S2, andthe computation region 12 may cover the one or more specific areas ofthe MRI image 10. For clear illustration of the following steps, FIGS.6A and 6B show the computation region 12 without the MRI image 10.Referring to FIG. 6A, in the step S3 of FIG. 4, after the computationregion 12 and the size and shape of the computation voxels of theprobability map are defined or determined, the stepping of the movingwindow 2 and the overlapping between two neighboring stops of the movingwindow 2 are determined. In the step S3, the moving window 2,illustrated in FIG. 3A, 3B or 3C for example, moves across thecomputation region 12 at a regular step or interval of a fixed distancein the x and y directions, and measures of specific MRI parameters(each, for example, may be the mean or a weighted mean) for each stop ofthe moving window 2 for the computation region 12 may be derived orobtained from the MRI image 10 or a registered imaging datasetincluding, e.g., the MRI image 10 and different MRI parameter mapsregistered to the MRI image 10. In an alternative example, the measuresfor some of the specific MRI parameters for each stop of the movingwindow 2 may be derived from different MRI parameter maps registered tothe MRI image 10, and the measures for the others may be derived fromthe same parameter map registered to the MRI image 10. The fixeddistance in the x direction may be substantially equal to the width Wsqin the case of the computation voxels defined as the squares with thewidth Wsq or may be substantially equal to the width Wrec in the case ofthe computation voxels defined as the rectangles with the width Wrec andthe length Lrec. The fixed distance in the y direction may besubstantially equal to the width Wsq in the case of the computationvoxels defined as the squares with the width Wsq or may be substantiallyequal to the length Lrec in the case of the computation voxels definedas the rectangles with the width Wrec and the length Lrec.

For more elaboration, referring to FIGS. 6A and 6B, the moving window 2may start at a corner Cx of the computation region 12. In the beginningof moving the moving window 2 across the computation region 12, thesquare 4 inscribed in the moving window 2 may have a corner Gx alignedwith the corner Cx of the computation region 12. In other words, thesquare 4 inscribed in the moving window 2 has an upper side 401 alignedwith an upper side 121 of the computation region 12 and a left side 402aligned with a left side 122 of the computation region 12. Twoneighboring stops of the moving window 2 that are shifted from eachother by the fixed distance in the x or y direction partially overlapeach other, and the ratio of the overlap of the two neighboring stops ofthe moving window 2 to the area of any one of the two neighboring stopsof the moving window 2 may range from, equal to or greater than 50percent up to, equal to or less than 99 percent.

The specific MRI parameters for each stop of the moving window 2 mayinclude T1 mapping, T2 raw signal, T2 mapping, delta Ktrans, tau, DtIVIM, fp IVIM, ADC (high b-values), nADC (high b-values), R*, Ktransfrom TM, ETM and SSM, and Ve from TM and SSM, which may be referred tothe types of the MRI parameters in the columns A-0 of the subset dataDB-1, respectively. Alternatively, the specific MRI parameters for eachstop of the moving window 2 may include four or more of the following:T1 mapping, T2 raw signal, T2 mapping, Ktrans from TM, ETM, and SSM, Vefrom TM and SSM, delta Ktrans, tau, ADC (high b-values), nADC (highb-values), Dt IVIM, fp IVIM, and R*. The specific MRI parameters ofdifferent modalities may be obtained from registered (multi-parametric)image sets (or the MRI parameter maps in the registered(multi-parametric) image dataset), and rigid and nonrigid standardregistration techniques may be used to get each section of anatomy intothe same exact coordinate location on each of the registered(multi-parametric) image sets (or on each of the MRI parameter maps).

Referring to FIG. 7A, the moving window 2 at each stop may cover oroverlap multiple voxels, e.g., 14 a through 14 f, in the computationregion 12, of the MRI image 10. A MRI parameter such as T1 mapping foreach stop of the moving window 2 may be calculated or measured bysumming values of the MRI parameter for the voxels 14 a-14 f weighed ormultiplied by the respective percentages of areas B1, B2, B3, B4, B5 andB6, overlapping with the respective voxels 14 a-14 f in the movingwindow 2, occupying the moving window 2. By this way, other MRIparameters (e.g., those in the columns B-O of the subset data DB-1) foreach stop of the moving window 2 are measured. Taking an example of T1mapping, in the case of the moving window 2 at a certain stop, values ofT1 mapping for the voxels 14 a-14 f and the percentages of the areasBI-B6 occupying the moving window 2 are assumed as shown in FIG. 7B. Ameasure, i.e., 1010.64, of T1 mapping for the stop of the moving window2 may be obtained or calculated by summing (1) the value, i.e., 1010, ofT1 mapping for the voxel 14a multiplied by the percentage, i.e., 6%, ofthe area B1, overlapping with the voxel 14 a in the moving window 2,occupying the moving window 2, (2) the value, i.e., 1000, of T1 mappingfor the voxel 14 b multiplied by the percentage, i.e., 38%, of the areaB2, overlapping with the voxel 14 b in the moving window 2, occupyingthe moving window 2, (3) the value, i.e., 1005, of T1 mapping for thevoxel 14 c multiplied by the percentage, i.e., 6%, of the area B3,overlapping with the voxel 14 c in the moving window 2, occupying themoving window 2, (4) the value, i.e., 1020, of T1 mapping for the voxel14 d multiplied by the percentage, i.e., 6%, of the area B4, overlappingwith the voxel 14 d in the moving window 2, occupying the moving window2, (5) the value, i.e., 1019, of T1 mapping for the voxel 14 emultiplied by the percentage, i.e., 38%, of the area B5, overlappingwith the voxel 14 e in the moving window 2, occupying the moving window2, and (6) the value, i.e., 1022, of T1 mapping for the voxel 14 fmultiplied by the percentage, i.e., 6%, of the area B6, overlapping withthe voxel 14 f in the moving window 2, occupying the moving window 2.Alternatively, the measure of each of the specific MRI parameters foreach stop of the moving window 2 may be the Gaussian weighted average ofmeasures, for said each of the specific MRI parameters, for the voxels,e.g., 14 a-14 f of the MRI image 10 overlapping with said each stop ofthe moving window 2.

The registered imaging dataset may be created for the subject toinclude, e.g., multiple registered MRI slice images (including, e.g.,MRI image 10) and/or corresponding MRI parameters obtained from variousequipment, machines, or devices or from a defined time-point (e.g.,specific date) or time range (e.g., within five days after treatment).Each of the MRI parameters in the subject's registered imaging datasetrequires alignment or registration. The registration can be done by, forexamples, using unique anatomical marks, structures, tissues, geometry,and/or shapes or using mathematical algorithms and computer patternrecognition. The measures of the specific imaging parameters for eachstop of the moving window 2, for example, may be obtained from theregistered imaging dataset for the subject.

Referring to FIG. 4, in the step S4 (optional), the reduction of the MRIparameters may be performed using, e.g., subset selection, aggregation,and dimensionality reduction so that a parameter set for each stop ofthe moving window 2 is obtained. The parameter set for each stop of themoving window 2 may include the measures for some of the specific MRIparameters from the step S3 (e.g., T1 mapping, T2 mapping, delta Ktrans,tau, Dt IVIM, fp IVIM, and R*) and values of average Ktrans (obtained byaveraging Ktrans from TM, Ktrans from ETM, and Ktrans from SSM) andaverage Ve (obtained by averaging Ve from TM and Ve from SSM). T2 rawsignal, ADC (high b-values), and nADC (high b-values) are not selectedinto the parameter set because the three MRI parameters are notdetermined to be conditionally independent. T1 mapping, T2 mapping,delta Ktrans, tau, Dt IVIM, fp IVIM, and R* are selected into theparameter set because the seven MRI parameters arc determined to beconditionally independent. Performing the step S4 may reduce parameternoise, create new parameters, and assure conditional independence neededfor (Bayesian) classification described in the step S5.

In the step S5, the parameter set for each stop of the moving window 2from the step S4 (or the measures of some or all of the specific MRIparameters for each stop of the moving window 2 from the step S3) may bematched to a biomarker library or the classifier CF for an event (e.g.,the first data type or feature depicted in the section of “descriptionof classifier CF”, or biopsy-diagnosed tissue characteristic for, e.g.,specific cancerous cells or occurrence of prostate or breast cancer)created based on data associated with the event from the subset dataDB-1. Accordingly, a probability PW of the event for each stop of themoving window 2 is obtained. In other words, the probability PW of theevent for each stop of the moving window 2 may be obtained based on theparameter set (from the step S4) or the measures of some or all of thespecific MRI parameters (from the step S3) for said each stop of themoving window 2 to match a matching dataset from the established orconstructed biomarker library or classifier CF. The biomarker library orclassifier CF, for example, may contain population-based information ofMRI imaging data and other information such as clinical and demographicdata for the event. In the invention, the probability PW of the eventfor each stop of the moving window 2 is assumed to be that for thesquare 4 inscribed in said each stop of the moving window 2.

In the step S6, an algorithm including steps S11 through S16 depicted inFIG. 8 is performed based on the probabilities PWs of the event for thestops of the moving window 2 to compute probabilities PVs of the eventfor the respective computation voxels, and the probabilities PVs of theevent for the respective computation voxels form the probability map.The probability map may be obtained in a short time (such as 10 minutesor 1 hour) after the MRI slice 10 obtained. To illustrate the algorithm,the moving window 2 may be defined to include at least four squares 6,as shown in FIG. 3A, 3B or 3C. Each of the squares 6 within the movingwindow 2, for example, may have an area less than 25% of that of themoving window 2. Two neighboring stops of the moving window 2, forexample, may have an overlapped region with an area ranging from 20% to99% of that of any one of the two neighboring stops of the moving window2, and some of the squares 6 inside each of the two neighboring stops ofthe moving window 2 may be within the overlapped region of the twoneighboring stops of the moving window 2. Alternatively, two neighboringstops of the moving window 2 may have an overlapped region with an arearanging from 1% to 20% of that of any one of the two neighboring stopsof the moving window 2. Referring to FIG. 8, in the step S11, theprobability PV of the event for each of the computation voxels isassumed by, e.g., averaging the probabilities PWs of the event for someof the stops of the moving window 2, each having one of the squares 6overlapping or covering said each of the computation voxels.

In the step S12, a probability guess PG for each stop of the movingwindow 2 is calculated by, e.g., averaging the probabilities PVs of theevent for all the computation voxels inside said each stop of the movingwidow 2. In the step S13, a difference DW between the probability guessPG and the probability PW of the event for each stop of the movingwindow 2 is calculated by, e.g., subtracting the probability PW of theevent for said each stop of the moving window 2 from the probabilityguess PG for said each stop of the moving window 2.

In the step S14, an absolute value of the difference DW between theprobability guess PG and the probability PW of the event for each stopof the moving window 2 is compared with a preset threshold error orvalue (e.g., 0.001 or 0.0001) to determine whether an error, i.e., theabsolute value of the difference DW, between the probability guess PGand the probability PW of the event for each stop of the moving window 2is less than or equal to the preset threshold error or value. If theabsolute value of the difference DW for each stop of the moving window 2is determined in the step S14 to be less than or equal to the presetthreshold error or value, the step S16 continues. In the step S16, theprobabilities PVs of the event for the computation voxels are determinedto be optimal, which arc called optimal probabilities hereinafter, andthe optimal probabilities of the respective computation voxels form theprobability map of the event for the MRI image 10 for the subject havingimaging information (e.g., MRI imaging information). After the optimalprobabilities for the respective computation voxels are obtained in thestep S16, the algorithm is completed.

If any one of the absolute values of the differences DWs for all thestops of the moving window 2 is determined in the step S14 to be greaterthan the preset threshold error or value, the step S15 continues. In thestep S15, the probability PV of the event for each of the computationvoxels is updated or adjusted by, e.g., subtracting an error correctionfactor ECF for said each of the computation voxels from the probabilityPV of the event for said each of the computation voxels. The errorcorrection factor ECF for each of the computation voxels is calculatedby, e.g., summing error correction contributions from the stops of themoving window 2 each having one of its squares 6 covering or overlappingsaid each of the computation voxels; each of the error correctioncontributions to said each of the computation voxels, for example, maybe calculated by multiplying the difference DW for a corresponding oneof the stops of the moving window 2 by an area ratio of an overlappedarea between said each of the computation voxels and the correspondingone of the stops of the moving window 2 to an area of the square 4inscribed in the corresponding one of the stops of the moving window 2.Alternatively, the error correction factor ECF for each of thecomputation voxels is calculated by, e.g., dividing the sum of thedifferences DWs for overlapping ones of the stops of the moving window2, each having one of its squares 6 covering or overlapping said each ofthe computation voxels, by the number of all the squares 6 within themoving window 2. After the probabilities PVs of the event for thecomputation voxels are updated, the steps S12-S15 are performedrepeatedly based on the updated probabilities PVs of the event for thecomputation voxels in the step S15, until the absolute value of thedifference DW between the probability guess PG and the probability PW ofthe event for each stop of the moving window 2 is determined in the stepS14 to be less than or equal to the preset threshold error or value.

The steps S12-S14 depicted in FIG. 8 may be performed N times, where Nis a positive integer, e.g., greater than 2, 5 or 10. In the first time,the steps S12-S14 are considered to perform the aforementioned stepsST2-ST4, respectively; in this case, the step S11 is considered toperform the aforementioned step ST1. In the second time, the stepsS12-S14 are considered to perform the aforementioned steps ST7-ST9,respectively; in this case, the step S15 is considered to perform theaforementioned steps ST5 and ST6. In the third through N times, thesteps S12-S14, as well as the step S15, arc considered to perform theaforementioned step ST 10. In addition, the step S16 is considered toperform the aforementioned step ST11.

For detailed description of the steps S11-S16, the square 4 inscribed inthe moving window 2 with the radius Rm is divided into, e.g., four smallsquares 6 each having width Wsq as shown in FIG. 3A, and in the step S2,the computation region 12 for the probability map is defined with, e.g.,nine computation voxels V1 through V9 shown in FIG. 9 based on the widthWsq of the four small squares 6 in the moving window 2. Each of the ninecomputation voxels V1-V9 used to compose the probability map is definedas a square with the width Wsq. Next, referring to FIGS. 10B, 10D, 10Fand 10H, the moving window 2 moves across the computation region 12 at aregular step or interval of a fixed distance in the x and y directions,and measures of the specific MRI parameters for four stops P₁₋₁, P₁₋₂,P₂₋₁ and P₂₋₂ of the moving window 2 are obtained from the MRI image 10or the registered imaging dataset. In the example, the fixed distance issubstantially equal to the width Wsq. Referring to FIGS. 10A and 10B,four small squares 6 a, 6 b, 6 c and 6 d, i.e., the four squares 6,within the square 4 inscribed in the stop P₁₋₁ of the moving window 2overlap or cover the four computation voxels V1, V2, V4 and V5,respectively, and each of the squares 6 a, 6 b, 6 c and 6 d has an arealess than 25% of that of the stop P₁₋₁ of the moving window 2. Referringto FIGS. 10C and 10D, four small squares 6 e, 6 f, 6 g and 6 h, i.e.,the four squares 6, within the square 4 inscribed in the stop P₁₋₂ ofthe moving window 2 overlap or cover the four computation voxels V2, V3,V5 and V6, respectively, and each of the squares 6 c, 6 f, 6 g and 6 hhas an area less than 25% of that of the stop P₁₋₂ of the moving window2. Referring to FIGS. 10E and 10F, four small squares 6 i, 6 j, 6 k and6 l, i.e., the four squares 6, within the square 4 inscribed in the stopof the movingwindow 2 overlap or cover the four computation voxels V4,V5, V7 and V8, respectively, and each of the squares 6 i, 6 j, 6 k and 6l has an area less than 25% of that of the stop P₂₋₁ of the movingwindow 2. Referring to FIGS. 10G and 10H, four small squares 6 m, 6 n, 6o and 6 p, i.e., the four squares 6, within the square 4 inscribed inthe stop P₂₋₂ of the moving window 2 overlap or cover the fourcomputation voxels V5, V6, V8 and V9, respectively, and each of thesquares 6 m, 6 n, 6 o and 6 p has an area less than 25% of that of thestop P₂₋₂ of the moving window 2. For details about the squares 6 a-6 p,please refer to the squares 6 illustrated in FIG. 3A.

After the measures of the specific MRI parameters for the stops P₁₋₂,P₂₋₁ and P₂₋₂ of the moving window 2 are obtained, the step S5 isperformed to obtain the probabilities PWs of the event for therespective stops P₁₋₁, P₁₋₂, P₂₋₁ and P₂₋₂ of the moving window 2. Theprobabilities PWs of the event for the four stops P₁₋₁, P₁₋₂, P₂₋₁ andP₂₋₂ of the moving window 2, for example, are 0.8166, 0.5928, 0.4407 and0.5586, respectively. In the example, the four probabilities PWs of theevent for the four stops P₁₋₁, P₁₋₂, P₂₋₁ and P₂₋₂ of the moving window2 are assumed to be those for the four squares 4 inscribed in therespective stops P₁₋₁, P₁₋₂, P₂₋₁ and P₂₋₂ of the moving window 2,respectively. In other words, the four probabilities of the event forthe four squares 4 inscribed in the four stops P₁₋₁, P₁₋₂, P₂₋₁ and P₂₋₂of the moving window 2 are 0.8166, 0.5928, 0.4407 and 0.5586,respectively.

Next, the algorithm depicted in FIG. 8 is performed based on theprobabilities PWs of the event for the respective stops P₁₋₁, P₁₋₂, P₂₋₁and P₂₋₂ of the moving window 2 to obtain or calculate optimalprobabilities of the event for the computation voxels V1-V9, asdescribed in the following specification. First of all, theprobabilities PVs of the event for the computation voxels V1-V9 as shownin FIG. 11A are assumed by the step S11. In the step S11, referring toFIGS. 10A-10H and 11A, because the only stop P₁₋₁ of the moving window 2has the square 6 a overlapping the computation voxel V1, the probabilityPV of the event for the computation voxel V1 is assumed to be theprobability PW, i.e., 0.8166, of the event for the stop P₁₋₁ of themoving window 2. Similarly, the probabilities PVs of the event for thecomputation voxels V3, V7 and V9 are assumed to be the probabilitiesPWs, i.e., 0.5928, 0.4407 and 0.5586, of the event for the stops P₁₋₂,P₂₋₁, and P₂₋₂ of the moving window 2, respectively. Because the onlytwo stops P₁₋₁ and P₁₋₂ of the moving window 2 have the squares 6 b and6 e overlapping the computation voxel V2, the probability PV of theevent for the computation voxel V2 is assumed to be the average, i.e.,0.7047, of the two probabilities PWs, i.e., 0.8166 and 0.5928, of theevent for the stops P₁₋₁ and P₁₋₂ of the moving window 2. Similarly, theprobability PV of the event for the computation voxel V4 is assumed tobe the average, i.e., 0.6286, of the probabilities PWs, i.e., 0.8166 and0.4407, of the event for the stops P₁₋₁ and P₂₋₁ of the moving window 2.The probability PV of the event for the computation voxel V6 is assumedto be the average, i.e., 0.5757, of the probabilities PWs, i.e., 0.5928and 0.5586, of the event for the stops P₁₋₂ and P₂₋₂ of the movingwindow 2. The probability PV of the event for the computation voxel V8is assumed to be the average, i.e., 0.4996, of the probabilities PWs,i.e., 0.4407 and 0.5586, of the event for the stops P₁₋₁ and P₂₋₂ of themoving window 2. Because the only four stops P₁₋₁, P₁₋₂, P₂₋₁ and P₂₋₂of the moving window 2 have four squares 6 d, 6 g, 6 j and 6 moverlapping the computation voxel V5, the probability PV of the eventfor the computation voxel V5 is assumed to be the average, i.e., 0.6022,of the four probabilities PWs, i.e., 0.8166, 0.5928, 0.4407 and 0.5586,of the event for the stops P₁₋₁, P₁₋₂, P₂₋₁ and P₂₋₂ of the movingwindow 2.

After the probabilities PVs of the event for the computation voxelsV1-V9 are assumed, the step S12 is performed to obtain a firstprobability guess PG, i.e., 0.6880, for the stop P₁₋₁ of the movingwindow 2, a second probability guess PG, i.e., 0.6188, for the stop P₁₋₂of the moving window 2, a third probability guess PG, i.e., 0.5428, forthe stop of the moving window 2, and a fourth probability guess PG, i.e.0.5590, for the stop P₂₋₂ of the moving window 2. The first probabilityguess PG for the stop P₁₋₁ of the moving window 2 is calculated byaveraging the probabilities PVs, i.e., 0.8166, 0.7047, 0.6286 and0.6022, of the event for the computation voxels V1, V2, V4 and V5 insidethe stop P₁₋₁ of the moving window 2. The second probability guess PGfor the stop P₁₋₂ of the moving window 2 is calculated by averaging theprobabilities PVs, i.e., 0.7047, 0.5928, 0.6022 and 0.5757, of the eventfor the computation voxels V2, V3, V5 and V6 inside the stop P₁₋₂ of themoving window 2. The third probability guess PG for the stop P₂₋₁ of themoving window 2 is calculated by averaging the probabilities PVs, i.e.,0.6286, 0.6022, 0.4407 and 0.4996, of the event for the computationvoxels V4, V5, V7 and V8 inside the stop P₂₋₁ of the moving window 2.The fourth probability guess PG for the stop P₂₋₂ of the moving window 2is calculated by averaging the probabilities PVs, i.e., 0.6022, 0.5757,0.4996 and 0.5586, of the event for the computation voxels V5, V6, V8and V9 inside the stop P₂₋₂ of the moving window 2.

After the first through fourth probability guesses PGs are obtained orcalculated, the step S13 is performed to obtain a first difference DWbetween the first probability guess PG and the probability PW of theevent for the stop P₁₋₁ of the moving window 2, a second difference DWbetween the second probability guess PG and the probability PW of theevent for the stop P₁₋₂ of the moving window 2, a third difference DWbetween the third probability guess PG and the probability PW of theevent for the stop P₂₋₁ of the moving window 2, and a fourth differenceDW between the fourth probability guess PG and the probability PW of theevent for the stop P₂₋₂ of the moving window 2. The first difference DW,i.e., −0.1286, is calculated by subtracting the probability PW, i.e.,0.8166, of the event for the stop P₁₋₁ of the moving window 2 from thefirst probability guess PG, i.e., 0.6880. The second difference DW,i.e., 0.0260, is calculated by subtracting the probability PW, i.e.,0.5928, of the event for the stop P₁₋₂ of the moving window 2 from thesecond probability guess PG, i.e., 0.6188. The third difference DW,i.e., 0.1021, is calculated by subtracting the probability PW, i.e.,0.4407, of the event for the stop P₂₋₁ of the moving window 2 from thethird probability guess PG, i.e., 0.5428. The fourth difference DW,i.e., 0.0004, is calculated by subtracting the probability PW, i.e.,0.5586, of the event for the stop P₂₋₂ from the fourth probability guessPG, i.e., 0.5590.

After the first through fourth differences DWs are obtained orcalculated, the step S14 is performed to determine whether absolutevalues of the first through fourth differences DWs arc less than orequal to a preset threshold value of 0.001. Because the absolute valuesof the first through third differences DWs are greater than the presetthreshold value, the step S15 continues in which the probabilities PVsof the event for the computation voxels V1-V9 are updated, as shown inFIG. 11B.

In the step S15, because the only stop P₁₋₁ of the moving window 2 hasthe square 6 a covering or overlapping the computation voxel V1, anerror correction factor ECF, i.e., −0.03215, for the computation voxelV1 is obtained by calculating an error correction contribution only fromthe stop P₁₋₁ of the moving window 2. The error correction contributionto the computation voxel V1 from the stop of the moving window 2 iscalculated by multiplying the first difference DW, i.e., −0.1286, forthe stop P₁₋₁ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V1 and the stop P₁₋₁ ofthe moving window 2 to an area of the square 4 inscribed in the stopP₁₋₁ of the moving window 2. Accordingly, the updated probability PV,i.e., 0.8488, of the event for the computation voxel V1 is calculated bysubtracting the error correction factor ECF, i.e., −0.03215, for thecomputation voxel V1 from the probability PV, i.e., 0.8166, of the eventfor the computation voxel V1. Similarly, the updated probability PV,i.e., 0.5863, of the event for the computation voxel V3 is calculated bysubtracting an error correction factor ECF, i.e., 0.0065, for thecomputation voxel V3 from the probability PV, i.e., 0.5928, of the eventfor the computation voxel V3, wherein the error correction factor ECFfor the computation voxel V3 is obtained by calculating an errorcorrection contribution only from the stop P₁₋₂ of the moving window 2.The error correction contribution to the computation voxel V3 from thestop P₁₋₂ of the moving window 2 is calculated by multiplying the seconddifference DW, i.e., 0.0260, for the stop P₁₋₂ of the moving window 2 byan area ratio, i.e., 1/4, of an overlapped area between the computationvoxel V3 and the stop P₁₋₂ of the moving window 2 to an area of thesquare 4 inscribed in the stop of the moving window 2. The updatedprobability PV, i.e., 0.4152, of the event for the computation voxel V7is calculated by subtracting an error correction factor ECF, i.e.,0.0255, for the computation voxel V7 from the probability PV, i.e.,0.4407, of the event for the computation voxel V7, wherein the errorcorrection factor ECF for the computation voxel V7 is obtained bycalculating an error correction contribution only from the stop P₂₋₁ ofthe moving window 2. The error correction contribution to thecomputation voxel V7 from the stop P₂₋₁ of the moving window 2 iscalculated by multiplying the third difference DW, i.e., 0.1021, for thestop P₂₋₁ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V7 and the stop PO₄ of themoving window 2 to an area of the square 4 inscribed in the stop P₂₋₁ ofthe moving window 2. The updated probability PV, i.e., 0.5585, of theevent for the computation voxel V9 is calculated by subtracting an errorcorrection factor ECF, i.e., 0.0001, for the computation voxel V9 fromthe probability PV, i.e., 0.5586, of the event for the computation voxelV9, wherein the error correction factor ECF for the computation voxel V9is obtained by calculating an error correction contribution only fromthe stop P₂₋₂ of the moving window 2. The error correction contributionto the computation voxel V9 from the stop P₂₋₂ of the moving window 2 iscalculated by multiplying the fourth difference DW, i.e., 0.0004, forthe stop P₂₋₂ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V9 and the stop P₂₋₂ ofthe moving window 2 to an area of the square 4 inscribed in the stopP₂₋₂ of the moving window 2.

In addition, because the only two stops P₁₋₁ and P₁₋₂ of the movingwindow 2 have the squares 6 b and 6 e covering or overlapping thecomputation voxel V2, an error correction factor ECF, i.e., −0.02565,for the computation voxel V2 is obtained by summing error correctioncontributions from the respective stops P₁₋₁ and P₁₋₂ of the movingwindow 2. The error correction contribution to the computation voxel V2from the stop P₁₋₁ of the moving window 2 is calculated by multiplyingthe first difference DW, i.e., −0.1286, for the stop P₁₋₁ of the movingwindow 2 by an area ratio, i.e., 1/4, of an overlapped area between thecomputation voxel V2 and the stop P₁₋₁ of the moving window 2 to thearea of the square 4 inscribed in the stop P₁₋₁ of the moving window 2.The error correction contribution to the computation voxel V2 from thestop P₁₋₂ of the moving window 2 is calculated by multiplying the seconddifference DW, i.e., 0.0260, for the stop P₁₋₂ of the moving window 2 byan area ratio, i.e., 1/4, of an overlapped area between the computationvoxel V2 and the stop of the moving window 2 to the area of the square 4inscribed in the stop P₁₋₂ of the moving window 2. Accordingly, theupdated probability PV, i.e., 0.7304, of the event for the computationvoxel V2 is calculated by subtracting the error correction factor ECF,i.e., −0.02565, for the computation voxel V2 from the probability PV,i.e., 0.7047, of the event for the computation voxel V2. Similarly, theupdated probability PV, i.e., 0.6352, of the event for the computationvoxel V4 is calculated by subtracting an error correction factor ECF,i.e., −0.006625, for the computation voxel V4 from the probability PV,i.e., 0.6286, of the event for the computation voxel V4, wherein theerror correction factor ECF for the computation voxel V4 is calculatedby summing error correction contributions from the respective stops P₁₋₁and P₂₋₁ of the moving window 2. The error correction contribution tothe computation voxel V4 from the stop P₁₋₁ of the moving window 2 iscalculated by multiplying the first difference DW, i.e., −0.1286, forthe stop P₁₋₁ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V4 and the stop P₁₋₁ ofthe moving window 2 to the area of the square 4 inscribed in the stopP₁₋₁ of the moving window 2. The error correction contribution to thecomputation voxel V4 from the stop P₂₋₁ of the moving window 2 iscalculated by multiplying the third difference DW, i.e., 0.1021, for thestop P₂₋₁ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V4 and the stop P₂₋₁ ofthe moving window 2 to the area of the square 4 inscribed in the stopP₂₋₁ of the moving window 2. The updated probability PV, i.e., 0.5691,of the event for the computation voxel V6 is calculated by subtractingan error correction factor ECF, i.e., 0.0066, for the computation voxelV6 from the probability PV, i.e., 0.5757, of the event for thecomputation voxel V6, wherein the error correction factor ECF for thecomputation voxel V6 is calculated by summing error correctioncontributions from the respective stops P₁₋₂ and P₂₋₂ of the movingwindow 2. The error correction contribution to the computation voxel V6from the stop P₁₋₂ of the moving window 2 is calculated by multiplyingthe second difference DW, i.e., 0.0260, for the stop P₁₋₂ of the movingwindow 2 by an area ratio, i.e., 1/4, of an overlapped area between thecomputation voxel V6 and the stop P₁₋₂ of the moving window 2 to thearea of the square 4 inscribed in the stop P₁₋₂ of the moving window 2.The error correction contribution to the computation voxel V6 from thestop P₂₋₂ of the moving window 2 is calculated by multiplying the fourthdifference DW, i.e., 0.0004, for the stop P₂₋₂ of the moving window 2 byan area ratio, i.e., 1/4, of an overlapped area between the computationvoxel V6 and the stop P₂₋₂ of the moving window 2 to the area of thesquare 4 inscribed in the stop of the moving window 2. The updatedprobability PV, i.e., 0.4740, of the event for the computation voxel V8is calculated by subtracting an error correction factor ECF, i.e.,0.025625, for the computation voxel V8 from the probability PV, i.e.,0.4996, of the event for the computation voxel V8, wherein the errorcorrection factor ECF for the computation voxel V8 is calculated bysumming error correction contributions from the respective stops P₂₋₁and P₂₋₂ of the moving window 2. The error correction contribution tothe computation voxel V8 from the stop P₂₋₁ of the moving window 2 iscalculated by multiplying the third difference DW, i.e., 0.1021, for thestop P₂₋₁ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V8 and the stop P₂₋₁ ofthe moving window 2 to the area of the square 4 inscribed in the stopP₂₋₁ of the moving window 2. The error correction contribution to thecomputation voxel V8 from the stop P₂₋₂ of the moving window 2 iscalculated by multiplying the fourth difference DW, i.e., 0.0004, forthe stop P₂₋₁ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V8 and the stop P₂₋₂ ofthe moving window 2 to the area of the square 4 inscribed in the stopP₂₋₂ of the moving window 2.

Because the only four stops P₁₋₁, P₁₋₂, P₂₋₁ and P₂₋₂ of the movingwindow 2 have the squares 6 d, 6 g, 6 j and 6 m covering or overlappingthe computation voxel V5, an error correction factor ECF, i.e.,−0.000025, for the computation voxel V5 is obtained by summing errorcorrection contributions from the respective stops P₁₋₁, P₁₋₂, P₂₋₁ andP₂₋₂ of the moving window 2. The error correction contribution to thecomputation voxel V5 from the stop P₁₋₁ of the moving window 2 iscalculated by multiplying the first difference DW, i.e., −0.1286, forthe stop P₁₋₁ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V5 and the stop P₁₋₁ ofthe moving window 2 to the area of the square 4 inscribed in the stopP₁₋₁ of the moving window 2. The error correction contribution to thecomputation voxel V5 from the stop P₁₋₂ of the moving window 2 iscalculated by multiplying the second difference DW, i.e., 0.0260, forthe stop P₁₋₂ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V5 and the stop P₁₋₂ ofthe moving window 2 to the area of the square 4 inscribed in the stopP₁₋₂ of the moving window 2. The error correction contribution to thecomputation voxel V5 from the stop P₁₋₂ of the moving window 2 iscalculated by multiplying the third difference DW, i.e., 0.1021, for thestop P₂₋₁ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V5 and the stop P₂₋₁ ofthe moving window 2 to the area of the square 4 inscribed in the stopP₂₋₁ of the moving window 2. The error correction contribution to thecomputation voxel V5 from the stop P₂₋₂ of the moving window 2 iscalculated by multiplying the fourth difference DW, i.e., 0.0004, forthe stop P₂₋₂ of the moving window 2 by an area ratio, i.e., 1/4, of anoverlapped area between the computation voxel V5 and the stop P₂₋₂ ofthe moving window 2 to the area of the square 4 inscribed in the stopP₂₋₂ of the moving window 2. Accordingly, the updated probability PV,i.e., 0.6022, of the event for the computation voxel V5 is calculated bysubtracting the error correction factor ECF, i.e., −0.000025, for thecomputation voxel V5 from the probability PV, i.e., 0.6022, of the eventfor the computation voxel V5.

After the updated probabilities PVs of the event for the computationvoxels V1-V9 are obtained or calculated, the steps S12-S15 are performedrepeatedly based on the updated probabilities PVs of the event for thecomputation voxels V1-V9 in the step S15, until the absolute values ofthe first through fourth differences DWs are less than or equal to thepreset threshold value. Accordingly, the optimal probabilities of theevent for the computation voxels V I-V9, as shown in FIG. 11C, areobtained and form the probability map for the event.

In an alternative example, the square 4 inscribed in the moving window 2with the radius Rm is divided into, e.g., nine small squares 6 eachhaving width Wsq as shown in FIG. 3B, and in the step S2, thecomputation region 12 for the probability map is defined with, e.g., 36computation voxels X1 through X36 as shown in FIG. 12 based on the widthWsq of the nine small squares 6 in the moving window 2. Each of the 36computation voxels X1-X36 used to compose the probability map is definedas a square with the width Wsq. Next, referring to FIGS. 13B, 13D, 13F,13H, 14B, 14D, 14F, 14H, 15B, 15D, 15F, 15H, 16B, 16D, 16F, and 16H, themoving window 2 moves across the computation region 12 at a regular stepor interval of a fixed distance in the x and y directions, and measuresof the specific MRI parameters for sixteen stops P₁₋₁, P₁₋₂, P₁₋₃, P₁₋₄,P₂₋₁, P₂₋₂, P₂₋₃, P₂₋₄, P₃₋₁, P₃₋₂, P₃₋₃, P₃₋₄, P₄₋₁, P₄₋₂, P₄₋₃, andP₄₋₄ of the moving window 2 are obtained from the MRI image 10 or theregistered imaging dataset. In the example, the fixed distance issubstantially equal to the width Wsq.

Referring to FIGS. 13A and 13B, nine small squares G1 through G9, i.e.,the nine squares 6, within the square 4 inscribed in the stops P₁₋₁ ofthe moving window 2 overlap or cover the nine computation voxels X1, X2,X3, X7, X8, X9, X13, X14 and X15, respectively, and each of the squaresG1 -G9 may have an area less than 10% of that of the stop P₁₋₁ of themoving window 2. For details about the squares G1-G9, please refer tothe squares 6 illustrated in FIG. 3B. Referring to FIGS. 13C and 13D,nine small squares G10 through G18, i.e., the nine squares 6, within thesquare 4 inscribed in the stop P₁₋₂ of the moving window 2 overlap orcover the nine computation voxels X2, X3, X4, X8, X9, X10, X14, X15 andX16, respectively, and each of the squares G10-G18 may have an area lessthan 10% of that of the stop P₁₋₂ of the moving window 2. For detailsabout the squares G10-G18, please refer to the squares 6 illustrated inFIG. 3B. Referring to FIGS. 13E and 13F, nine small squares G19 throughG27, i.e., the nine squares 6, within the square 4 inscribed in the stopP₁₋₃ of the moving window 2 overlap or cover the nine computation voxelsX3, X4, X5, X9, X10, X11, X15, X16 and X17, respectively, and each ofthe squares G19-G27 may have an area less than 10% of that of the stopP₁₋₃ of the moving window 2. For details about the squares G19-G27,please refer to the squares 6 illustrated in FIG. 3B. Referring to FIGS.13G and 13H, nine small squares G28 through G36, i.e., the nine squares6, within the square 4 inscribed in the stop P₁₋₄ of the moving window 2overlap or cover the nine computation voxels X4, X5, X6, X10, X11, X12,X16, X17 and X18, respectively, and each of the squares G28-G36 may havean area less than 10% of that of the stop P₁₋₄ of the moving window 2.For details about the squares G28-G36, please refer to the squares 6illustrated in FIG. 3B.

Referring to FIGS. 14A and 14B, nine small squares G37 through G45,i.e., the nine squares 6, within the square 4 inscribed in the stop P₂₋₁of the moving window 2 overlap or cover the nine computation voxels X7,X8, X9, X13, X14, X15, X19, X20 and X21, respectively, and each of thesquares G37-G45 may have an area less than 10% of that of the stop P₂₋₁of the moving window 2. For details about the squares G37-G45, pleaserefer to the squares 6 illustrated in FIG. 3B. Referring to FIGS. 14Cand 14D, nine small squares G46 through G54, i.e., the nine squares 6,within the square 4 inscribed in the stop P₂₋₂ of the moving window 2overlap or cover the nine computation voxels X8, X9, X10, X14, X15, X16,X20, X21 and X22, respectively, and each of the squares G46-G54 may havean area less than 10% of that of the stop P₂₋₂ of the moving window 2.For details about the squares G46-G54, please refer to the squares 6illustrated in FIG. 3B. Referring to FIGS. 14E and 14F, nine smallsquares G55 through G63, i.e., the nine squares 6, within the square 4inscribed in the stop of the moving window 2 overlap or cover the ninecomputation voxels X9, X10, X11, X15, X16, X17, X21, X22 and X23,respectively, and each of the squares G55-G63 may have an area less than10% of that of the stop P₂₋₃ of the moving window 2. For details aboutthe squares G55-G63, please refer to the squares 6 illustrated in FIG.3B. Referring to FIGS. 14G and 14H, nine small squares G64 through G72,i.e., the nine squares 6, within the square 4 inscribed in the stop P₂₋₄of the moving window 2 overlap or cover the nine computation voxels X10,X11, X12, X16, X17, X18, X22, X23 and X24, respectively, and each of thesquares G64-G72 may have an area less than 10% of that of the stop P₂₋₄of the moving window 2. For details about the squares G64-G72, pleaserefer to the squares 6 illustrated in FIG. 3B.

Referring to FIGS. 15A and 15B, nine small squares G73 through G81,i.e., the nine squares 6, within the square 4 inscribed in the stop P₃₋₁of the moving window 2 overlap or cover the nine computation voxels X13,X14, X15, X19, X20, X21, X25, X26 and X27, respectively, and each of thesquares G73-G81 may have an area less than 10% of that of the stop P₃₋₁of the moving window 2. For details about the squares G73-G81, pleaserefer to the squares 6 illustrated in FIG. 3B. Referring to FIGS. 15Cand 15D, nine small squares G82 through G90, i.e., the nine squares 6,within the square 4 inscribed in the stop P₃₋₂ of the moving window 2overlap or cover the nine computation voxels X14, X15, X16, X20, X21,X22, X26, X27 and X28, respectively, and each of the squares G82-G90 mayhave an area less than 10% of that of the stop P₃₋₂ of the moving window2. For details about the squares G82-G90, please refer to the squares 6illustrated in FIG. 3B. Referring to FIGS. 15E and 15F, nine smallsquares G91 through G99, i.e., the nine squares 6, within the square 4inscribed in the stop P₃₋₃ of the moving window 2 overlap or cover thenine computation voxels X15, X16, X17, X21, X22, X23, X27, X28 and X29,respectively, and each of the squares G91-G99 may have an area less than10% of that of the stop P₃₋₃ of the moving window 2. For details aboutthe squares G91-G99, please refer to the squares 6 illustrated in FIG.3B. Referring to FIGS. 15G and 15H, nine small squares G100 throughG108, i.e., the nine squares 6, within the square 4 inscribed in thestop P₃₋₄ of the moving window 2 overlap or cover the nine computationvoxels X16, X17, X18, X22, X23, X24, X28, X29 and X30, respectively, andeach of the squares G100-G108 may have an area less than 10% of that ofthe stop P₃₋₄ of the moving window 2. For details about the squaresG100-G108, please refer to the squares 6 illustrated in FIG. 3B.

Referring to FIGS. 16A and 16B, nine small squares G109 through G117,i.e., the nine squares 6, within the square 4 inscribed in the stop P₄₋₁of the moving window 2 overlap or cover the nine computation voxels X19,X20, X21, X25, X26, X27, X31, X32 and X33, respectively, and each of thesquares G109-G117 may have an area less than 10% of that of the stopP₄₋₁ of the moving window 2. For details about the squares G109-G117,please refer to the squares 6 illustrated in FIG. 3B. Referring to FIGS.16C and 16D, nine small squares G118 through G126, i.e., the ninesquares 6, within the square 4 inscribed in the stop P₄₋₂ of the movingwindow 2 overlap or cover the nine computation voxels X20, X21, X22,X26, X27, X28, X32, X33 and X34, respectively, and each of the squaresG118-G126 may have an area less than 10% of that of the stop P₄₋₂ of themoving window 2. For details about the squares G118-G126, please referto the squares 6 illustrated in FIG. 3B. Referring to FIGS. 16E and 16F,nine small squares G127 through G135, i.e., the nine squares 6, withinthe square 4 inscribed in the stop P₄₋₃ of the moving window 2 overlapor cover the nine computation voxels X21, X22, X23, X27, X28, X29, X33,X34 and X35, respectively, and each of the squares G127-G135 may have anarea less than 10% of that of the stop P₄₋₃ of the moving window 2. Fordetails about the squares G127-G135, please refer to the squares 6illustrated in FIG. 3B. Referring to FIGS. 16G and 16H, nine smallsquares G136 through G144, i.e., the nine squares 6, within the square 4inscribed in the stop P₄₋₄ of the moving window 2 overlap or cover thenine computation voxels X22, X23, X24, X28, X29, X30, X34, X35 and X36,respectively, and each of the squares G136-G144 may have an area lessthan 10% of that of the stop P₄₋₄ of the moving window 2. For detailsabout the squares G136-G144, please refer to the squares 6 illustratedin FIG. 3B.

After the measures of the specific MRI parameters for the sixteen stopsP₁₋₁-P₄₋₄ of the moving window 2 are obtained, the step S5 is performedto obtain the probabilities PWs of the event for the respective stopsP₁₋₁-P₄₋₄ of the moving window 2. The probabilities PWs of the event forthe sixteen stops P₁₋₄, P₁₋₂, P₁₋₃, P₁₋₄, P₂₋₁, P₂₋₂, P₂₋₃, P₂₋₄, P₃₋₁,P₃₋₂, P₃₋₃, P₃₋₄, P₄₋₁, P₄₋₂, P₄₋₃, and P₄₋₄ of the moving window 2, forexample, are 0.6055, 0.5628, 0.5366, 0.4361, 0.4982, 0.5534, 0.5521,0.4227, 0.4618, 0.5132, 0.6214, 0.5810, 0.4371, 0.4698, 0.5774, and0.5613, respectively. In the example, the sixteen probabilities PWs ofthe event for the sixteen stops P₁₋₁-P₄₋₄ of the moving window 2 areassumed to be those for the sixteen squares 4 inscribed in therespective stops P₁₋₁-P₄₋₄ of the moving window 2, respectively. Inother words, the sixteen probabilities of the event for the sixteensquares 4 inscribed in the sixteen stops P₁₋₁-P₄₋₄ of the moving window2 are 0.6055, 0.5628, 0.5366, 0.4361, 0.4982, 0.5534, 0.5521, 0.4227,0.4618, 0.5132, 0.6214, 0.5810, 0.4371, 0.4698, 0.5774, and 0.5613,respectively.

Next, the algorithm depicted in FIG. 8 is performed based on theprobabilities PWs of the event for the sixteen stops P₁₋₁-P₄₋₄ of themoving window 2 to obtain or calculate optimal probabilities of theevent for the computation voxels X1-X36, as described in the followingspecification. First of all, the probabilities PVs of the event for thecomputation voxels X1-X36 as shown in FIG. 17A are assumed by the stepS11. In the step S11, referring to FIGS. 13A-13H, 14A-14H, 15A-15H,16A-16H, and 17A, because the only stop P₁₋₁ of the moving window 2 hasthe square G1 overlapping the computation voxel X1, the probability PVof the event for the computation voxel X1 is assumed to be theprobability PW, i.e., 0.6055, of the event for the stop P₁₋₁ of themoving window 2. Similarly, the probabilities PVs of the event for thecomputation voxels X6, X31 and X36 are assumed to be the probabilitiesPWs, i.e., 0.4361, 0.4371 and 0.5613, of the event for the stops P₁₋₄,P₄₋₁, and P₄₋₄ of the moving window 2, respectively.

Because the only two stops P₁₋₁ and P₁₋₂ of the moving window 2 have thesquares G2 and G10 overlapping the computation voxel X2, the probabilityPV of the event for the computation voxel X2 is assumed to be theaverage, i.e., 0.5841, of the two probabilities PWs, i.e., 0.6055 and0.5628, of the event for the stops P₁₋₁ and P₁₋₂ of the moving window 2.Similarly, the probability PV of the event for the computation voxel X5is assumed to be the average, i.e., 0.4863, of the probabilities PWs,i.e., 0.5366 and 0.4361, of the event for the stops P₁₋₃ and P₁₋₄ of themoving window 2. The probability PV of the event for the computationvoxel X7 is assumed to be the average, i.e., 0.5519, of theprobabilities PWs, i.e., 0.6055 and 0.4982, of the event for the stopsP₁₋₁ and P₂₋₁ of the moving window 2. The probability PV of the eventfor the computation voxel X12 is assumed to be the average, i.e.,0.4294, of the probabilities PWs, i.e., 0.4361 and 0.4227, of the eventfor the stops P₁₋₄ and P₂₋₄ of the moving window 2. The probability PVof the event for the computation voxel X25 is assumed to be the average,i.e., 0.4495, of the probabilities PWs, i.e., 0.4618 and 0.4371, of theevent for the stops P₃₋₁ and P₄₋₁ of the moving window 2. Theprobability PV of the event for the computation voxel X30 is assumed tobe the average, i.e., 0.5711, of the probabilities PWs, i.e., 0.5810 and0.5613, of the event for the stops P₃₋₄ and P₄₋₄ of the moving window 2.The probability PV of the event for the computation voxel X32 is assumedto be the average, i.e., 0.4535, of the probabilities PWs, i.e., 0.4371and 0.4698, of the event for the stops P₄₋₁ and P₄₋₂ of the movingwindow 2. The probability PV of the event for the computation voxel X35is assumed to be the average, i.e., 0.5693, of the probabilities PWs,i.e., 0.5774 and 0.5613, of the event for the stops P₄₋₃ and P₄₋₄ of themoving window 2.

Because the only three stops P₁₋₁, P₁₋₂ and P₁₋₃ of the moving window 2have the squares G3, G11 and G19 overlapping the computation voxel X3,the probability PV of the event for the computation voxel X3 is assumedto be the average, i.e., 0.5683, of the three probabilities PWs, i.e.,0.6055, 0.5628 and 0.5366, of the event for the stops P₁₋₁, P₁₋₂ andP₁₋₃ of the moving window 2. Similarly, the probability PV of the eventfor the computation voxel X4 is assumed to be the average, i.e., 0.5118,of the probabilities PWs of the event for the stops P₁₋₂, P₁₋₃ and P₁₋₄of the moving window 2. The probability PV of the event for thecomputation voxel X13 is assumed to be the average, i.e., 0.5219, of theprobabilities PWs of the event for the stops P₁₋₁, P₂₋₁ and P₃₋₁ of themoving window 2. The probability PV of the event for the computationvoxel X18 is assumed to be the average, i.e., 0.4799, of theprobabilities PWs of the event for the stops P₁₋₄, P₂₋₄ and P₃₋₄ of themoving window 2. The probability PV of the event for the computationvoxel X19 is assumed to be the average, i.e., 0.4657, of theprobabilities PWs of the event for the stops P₂₋₁, P₃₋₁ and P₄₋₁ of themoving window 2. The probability PV of the event for the computationvoxel X24 is assumed to be the average, i.e., 0.5216, of theprobabilities PWs of the event for the stops P₂₋₄, P₃₋₄ and P₄₋₄ of themoving window 2. The probability PV of the event for the computationvoxel X33 is assumed to be the average, i.e., 0.4948, of theprobabilities PWs of the event for the stops P₄₋₁, P₄₋₂ and P₄₋₃ of themoving window 2. The probability PV of the event for the computationvoxel X34 is assumed to be the average, i.e., 0.5362, of theprobabilities PWs of the event for the stops P₄₋₂, P₄₋₃ and P₄₋₄ of themoving window 2.

Because the only four stops P₁₋₁, P₁₋₂, P₂₋₁ and P₂₋₂ of the movingwindow 2 have the squares G5, G13, G38 and G46 overlapping thecomputation voxel X8, the probability PV of the event for thecomputation voxel X8 is assumed to be the average, i.e., 0.5550, of thefour probabilities PWs, i.e., 0.6055, 0.5628, 0.4982 and 0.5534, of theevent for the stops P₁₋₁, P₁₋₂, P₂₋₁ and P₂₋₂ of the moving window 2.Similarly, the probability PV of the event for the computation voxel X11is assumed to be the average, i.e., 0.4869, of the probabilities PWs ofthe event for the stops P₁₋₃, P₁₋₄, P₂₋₃ and P₂₋₄ of the moving window2. The probability PV of the event for the computation voxel X26 isassumed to be the average, i.e., 0.4705, of the probabilities PWs of theevent for the stops P₃₋₁, P₃₋₂, P₄₋₁ and P₄₋₂ of the moving window 2.The probability PV of the event for the computation voxel X29 is assumedto be the average, i.e., 0.5852, of the probabilities PWs of the eventfor the stops P₃₋₃, P₃₋₄, P₄₋₃ and P₄₋₄ of the moving window 2.

Because the only six stops P₁₋₁, P₁₋₂, P₁₋₃, P₂₋₁, P₂₋₂ and P₂₋₃ of themoving window 2 have the squares G6, G14, G22, G39, G47 and G55overlapping the computation voxel X9, the probability PV of the eventfor the computation voxel X9 is assumed to be the average, i.e., 0.5514,of the six probabilities PWs, i.e., 0.6055, 0.5628, 0.5366, 0.4982,0.5534 and 0.5521, of the event for the stops P₁₋₁, P₁₋₂, P₁₋₃, P₂₋₁,P₂₋₂ and P₂₋₃ of the moving window 2. Similarly, the probability PV ofthe event for the computation voxel X10 is assumed to be the average,i.e., 0.5106, of the probabilities PWs of the event for the stops P₁₋₂,P₁₋₃, P₁₋₄, P₂₋₂, P₂₋₃ and P₂₋₄ of the moving window 2. The probabilityPV of the event for the computation voxel X14 is assumed to be theaverage, i.e., 0.5325, of the probabilities PWs of the event for thestops P₁₋₁, P₁₋₂, P₂₋₁, P₂₋₂, P₃₋₁ and P₃₋₂ of the moving window 2. Theprobability PV of the event for the computation voxel X17 is assumed tobe the average, i.e., 0.5250, of the probabilities PWs of the event forthe stops P₁₋₃, P₁₋₄, P₂₋₃, P₂₋₄, P₃₋₃ and P₃₋₄ of the moving window 2.The probability PV of the event for the computation voxel X20 is assumedto be the average, i.e., 0.4889, of the probabilities PWs of the eventfor the stops P₂₋₁, P₂₋₂, P₃₋₁, P₃₋₂, P₄₋₁ and P₄₋₂ of the moving window2. The probability PV of the event for the computation voxel X23 isassumed to be the average, i.e., 0.5526, of the probabilities PWs of theevent for the stops P₂₋₃, P₂₋₄, P₃₋₃, P₃₋₄, P₄₋₃ and P₄₋₄ of the movingwindow 2. The probability PV of the event for the computation voxel X27is assumed to be the average, i.e., 0.5134, of the probabilities PWs ofthe event for the stops P₃₋₁, P₃₋₂, P₃₋₃, P₄₋₁, P₄₋₂ and P₄₋₃ of themoving window 2. The probability PV of the event for the computationvoxel X28 is assumed to be the average, i.e., 0.5540, of theprobabilities PWs of the event for the stops P₃₋₂, P₃₋₃, P₃₋₄, P₄₋₂,P₄₋₃ and P₄₋₄ of the moving window 2.

Because the only nine stops P₁₋₁, P₁₋₂, P₁₋₃, P₂₋₁, P₂₋₂, P₂₋₃, P₃₋₁,P₃₋₂ and P₃₋₃ of the moving window 2 have the squares G9, G17, G25, G42,G50, G58, G75, G83 and G91 overlapping the computation voxel X15, theprobability PV of the event for the computation voxel X15 is assumed tobe the average, i.e., 0.5450, of the nine probabilities PWs, i.e.,0.6055, 0.5628, 0.5366, 0.4982, 0.5534, 0.5521, 0.4618, 0.5132 and0.6214, of the event for the stops P₁₋₁, P₁₋₂, P₁₋₃, P₂₋₁, P₂₋₂, P₂₋₃,P₃₋₁, P₃₋₂ and P₃₋₃ of the moving window 2. Similarly, the probabilityPV of the event for the computation voxel X16 is assumed to be theaverage, i.e., 0.5310, of the probabilities PWs of the event for thestops P₁₋₂, P₁₋₃, P₁₋₄, P₂₋₂, P₂₋₃, P₂₋₄, P₃₋₂, P₃₋₃ and P₃₋₄ of themoving window 2. The probability PV of the event for the computationvoxel X21 is assumed to be the average, i.e., 0.5205, of theprobabilities PWs of the event for the stops P₂₋₁, P₂₋₂, P₂₋₃, P₃₋₁,P₃₋₂, P₃₋₃, P₄₋₁, P₄₋₂ and P₄₋₃ of the moving window 2. The probabilityPV of the event for the computation voxel X22 is assumed to be theaverage, i.e., 0.5391, of the probabilities PWs of the event for thestops P₂₋₂, P₂₋₃, P₂₋₄, P₃₋₂, P₃₋₃, P₃₋₄, P₄₋₂, P₄₋₃ and P₄₋₄ of themoving window 2.

After the probabilities PVs of the event for the respective computationvoxels X1-X36 are assumed, the step S12 is performed to obtain sixteenprobability guesses PGs for the respective stops P₁₋₁, P₁₋₂, P₁₋₃, P₁₋₄,P₂₋₁, P₂₋₂, P₂₋₃, P₂₋₄,P₃₋₁, P₃₋₂, P₃₋₃, P₃₋₄, P₄₋₁, P₄₋₂, P₄₋₃, andP₄₋₄ of the moving window 2. The probability guess PG for each of thesixteen stops P₁₋₁-P₄₋₄ of the moving window 2 is calculated byaveraging the nine probabilities PVs of the event for respective nine ofthe computation voxels X1-X36 overlapping or covering the respectivenine small squares 6 within the square 4 inscribed in said each of thesixteen stops P₁₋₁-P₄₋₄ of the moving window 2. For example, because thenine small squares G1-G9 within the square 4 inscribed in the stop P₁₋₁of the moving window 2 overlap or cover the respective computationvoxels X1, X2, X3, X7, X8, X9, X13, X14 and X15, the probability guessPG for the stop P₁₋₁ of the moving window 2 is calculated by averagingthe nine probabilities PVs, i.e., 0.6055, 0.5841, 0.5683, 0.5519,0.5550, 0.5514, 0.5219, 0.5325 and 0.5450, of the event for thecomputation voxels X1, X2, X3, X7, X8, X9, X13, X14 and X15 inside thestop P₁₋₁ of the moving window 2. Accordingly, the probability guessesPGs for the stops P₁₋₁, P₁₋₂, P₁₋₃, P₁₋₄, P₂₋₁, P₂₋₂, P₂₋₃, P₂₋₄, P₃₋₁,P₃₋₂, P₃₋₃, P₃₋₄, P₄₋₁, P₄₋₂, P₄₋₃, and P₄₋₄ of the moving window 2 are0.5573, 0.5433, 0.5240, 0.4886, 0.5259, 0.5305, 0.5291, 0.5085, 0.5009,0.5217, 0.5407, 0.5400, 0.4771, 0.5079, 0.5406, and 0.5545,respectively.

After the sixteen probability guesses PGs are obtained or calculated,the step S13 is performed to obtain sixteen differences DWs for thesixteen stops P₁₋₁-P₄₋₄ of the moving window 2. Each of the sixteendifferences DWs is calculated by, e.g., subtracting the probability PWof the event for a corresponding one of the sixteen stops P₁₋₁-P₄₋₄ ofthe moving window 2 from the probability guess PG for the correspondingone of the sixteen stops P₁₋₁-P₄₋₄ of the moving window 2. For example,the difference DW for the stop P₁₋₁ of the moving window 2 is calculatedby subtracting the probability PW, i.e., 0.6055, of the event for thestop P₁₋₁ of the moving window 2 from the probability guess PG, i.e.,0.5573, for the stop P₁₋₁ of the moving window 2. Accordingly, thedifferences DWs for the stops P₁₋₁, P₁₋₂, P₁₋₃, P₁₋₄, P₂₋₁, P₂₋₂, P₂₋₃,P₂₋₄, P₃₋₁, P₃₋₂, P₃₋₃, P₃₋₄, P₄₋₁, P₄₋₂, P₄₋₃, and P₄₋₄ of the movingwindow 2 are −0.0482, −0.0194, −0.0126, 0.0525, 0.0276, −0.0230,−0.0230, 0.0858, 0.0391, 0.0085, −0.0807, −0.0410, 0.0400, 0.0380,−0.0368, and −0.0068, respectively.

After the sixteen differences DWs are obtained or calculated, the stepS14 is performed to determine whether absolute values of the sixteendifferences DWs are less than or equal to a preset threshold value of0.0001. Because the absolute values of the sixteen differences DWs aregreater than the preset threshold value, the step S15 continues in whichthe probabilities PVs of the event for the computation voxels X1-X36 areupdated, as shown in FIG. 17B.

In the step S15, the updated probability PV of the event for each of thecomputation voxels X1-X36 is calculated by, e.g., subtracting an errorcorrection factor ECF for said each of the computation voxels X1-X36from the probability PV of the event for said each of the computationvoxels X1-X36. The error correction factor ECF for each of the 4computation voxels X1, X6, X31 and X36 is obtained by, e.g., calculatingan error correction contribution only from a corresponding one of thestops P₁₋₁, P₁₋₄, P₄₋₄ and P₄₋₄ of the moving window 2, which has one ofits squares 6 covering or overlapping said each of the 4 computationvoxels X1, X6, X31 and X36. For example, because the only stop P₁₋₁ ofthe moving window 2 has the small square G1 covering or overlapping thecomputation voxel X1 the error correction factor ECF, i.e., -0.0054, forthe computation voxel X1 is obtained by calculating the error correctioncontribution only from the stop P₁₋₁ of the moving window 2. The errorcorrection contribution to the computation voxel X1 from the stop P₁₋₁of the moving window 2 is calculated by multiplying the difference DW,i.e., −0.0482, for the stop P₁₋₁ of the moving window 2 by an arearatio, i.e., 1/9, of an overlapped area between the computation voxel X1and the stop P₁₋₁ of the moving window 2 to an area of the square 4inscribed in the stop P₁₋₁ of the moving window 2. Accordingly, theupdated probability PV of the event for the computation voxel X1 iscalculated by subtracting the error correction factor ECF, i.e.,−0.0054, for the computation voxel X1 from the probability PV, i.e.,0.6055, of the event for the computation voxel X1.

The error correction factor ECF for each of the 32 computation voxelsX2-X5, X7-X30 and X32-X35 is calculated by, e.g., summing errorcorrection contributions from overlapping ones of the stops P₁₋₁-P₄₋₄ ofthe moving window 2, each having one of its squares 6 covering oroverlapping said each of the 32 computation voxels X2-X5, X7-X30 andX32-X35; each of the error correction contributions to said each of the32 computation voxels X2-X5, X7-X30 and X32-X35 is calculated bymultiplying the difference DW for a corresponding one of the overlappingones of the stops P₁₋₁-P₄₋₄ of the moving window 2 by an area ratio ofan overlapped area between said each of the 32 computation voxels X2-X5,X7-X30 and X32-X35 and the corresponding one of the overlapping ones ofthe stops P₁₋₁-P₄₋₄ of the moving window 2 to an area of the square 4inscribed in the corresponding one of the overlapping ones of the stopsP₁₋₁-P₄₋₄ of the moving window 2. For example, because the only ninestops P₁₋₁, P₁₋₂, P₁₋₃, P₂₋₁, P₂₋₂, P₂₋₃, P₃₋₁, and P₃₋₃ of the movingwindow 2 have the squares G9, G17, G25, G42, G50, G58, G75, G83 and G91covering or overlapping the computation voxel X15, the error correctionfactor ECF, i.e., −0.0146, for the computation voxel X15 is obtained bysumming error correction contributions from the respective stops P₁₋₁,P₁₋₂, P₁₋₃, P₂₋₁, P₂₋₂, P₂₋₃, P₃₋₁, P₃₋₂, and P₃₋₃ of the moving window2. The error correction contribution, i.e., −0.0053, from the stop P₁₋₁of the moving window 2 to the computation voxel X15 is calculated bymultiplying the difference DW, i.e., −0.0482, for the stop P₁₋₁ of themoving window 2 by an area ratio, i.e., 1/9, of an overlapped areabetween the computation voxel X15 and the stop P₁₋₁ of the moving window2 to the area of the square 4 inscribed in the stop P₁₋₁ of the movingwindow 2. The error correction contribution, i.e., −0.0021, from thestop P₁₋₂ of the moving window 2 to the computation voxel X15 iscalculated by multiplying the difference DW, i.e., −0.0194, for the stopP₁₋₂ of the moving window 2 by an area ratio, i.e., 1/9, of anoverlapped area between the computation voxel X15 and the stop P₁₋₂ ofthe moving window 2 to the area of the square 4 inscribed in the stopP₁₋₂ of the moving window 2. The error correction contribution, i.e.,−0.0014, from the stop P₁₋₃ of the moving window 2 to the computationvoxel X15 is calculated by multiplying the difference DW, i.e., −0.0126,for the stop P₁ of the moving window 2 by an area ratio, i.e., 1/9, ofan overlapped area between the computation voxel X15 and the stop P₁₋₃of the moving window 2 to the area of the square 4 inscribed in the stopP₁₋₃ of the moving window 2. The error correction contribution, i.e.,0.0031, from the stop P₂₋₁ of the moving window 2 to the computationvoxel X15 is calculated by multiplying the difference DW, i.e., 0.0276,for the stop P₂₋₁ of the moving window 2 by an area ratio, i.e., 1/9, ofan overlapped area between the computation voxel X15 and the stop P₂₋₁of the moving window 2 to the area of the square 4 inscribed in the stopP₂₋₁ of the moving window 2. The error correction contribution, i.e.,−0.0026, from the stop P₂₋₂ of the moving window 2 to the computationvoxel X15 is calculated by multiplying the difference DW, i.e., −0.0230,for the stop P₂₋₂ of the moving window 2 by an area ratio, i.e., 1/9, ofan overlapped area between the computation voxel X15 and the stop P₂₋₂of the moving window 2 to the area of the square 4 inscribed in the stopP₂₋₂ of the moving window 2. The error correction contribution, i.e.,−0.0026, from the stop P₂₋₃ of the moving window 2 to the computationvoxel X15 is calculated by multiplying the difference DW, i.e., −0.0230,for the stop P₂₋₃ of the moving window 2 by an area ratio, i.e., 1/9, ofan overlapped area between the computation voxel X15 and the stop P₂₋₃of the moving window 2 to the area of the square 4 inscribed in the stopP₂₋₃ of the moving window 2. The error correction contribution, i.e.,0.0043, from the stop of the moving window 2 to the computation voxelX15 is calculated by multiplying the difference DW, i.e., 0.0391, forthe stop P₃₋₁ of the moving window 2 by an area ratio, i.e., 1/9, of anoverlapped area between the computation voxel X15 and the stop P₃₋₁ ofthe moving window 2 to the area of the square 4 inscribed in the stopP₃₋₁ of the moving window 2. The error correction contribution, i.e.,0.0009, from the stop P₃₋₂ of the moving window 2 to the computationvoxel X15 is calculated by multiplying the difference DW, i.e., 0.0085,for the stop P₃₋₂ of the moving window 2 by an area ratio, i.e., 1/9, ofan overlapped area between the computation voxel X15 and the stop P₃₋₂of the moving window 2 to the area of the square 4 inscribed in the stopP₃₋₂ of the moving window 2. The error correction contribution, i.e.,−0.0089, from the stop P₃₋₃ of the moving window 2 to the computationvoxel X15 is calculated by multiplying the difference DW, i.e., −0.0807,for the stop P₃₋₃ of the moving window 2 by an area ratio, i.e., 1/9, ofan overlapped area between the computation voxel X15 and the stop P₃₋₃of the moving window 2 to the area of the square 4 inscribed in the stopP₃₋₃ of the moving window 2. Accordingly, the updated probability PV ofthe event for the computation voxel X15 is calculated by subtracting theerror correction factor ECF, i.e., −0.0146, for the computation voxelX15 from the probability PV, i.e., 0.5450, of the event for thecomputation voxel X15.

After the updated probabilities PVs of the event for the computationvoxels X1-X36 are obtained or calculated, the steps S12-S15 areperformed repeatedly based on the updated probabilities PVs of the eventfor the computation voxels X1-X36 in the step S15, until the absolutevalues of the sixteen differences DWs for the sixteen stops P₁₋₁-P₄₋₄ ofthe moving window 2 are less than or equal to the preset thresholdvalue. Accordingly, the optimal probabilities of the event for thecomputation voxels X1-X36, as shown in FIG. 17C, are obtained and formthe probability map for the event.

The above process, including the steps S1-S6, is performed to generatethe moving window 2 across the computation regions 12 of the MRI slice10 along the x and y directions to create a two-dimensional (2D)probability map. In order to obtain a three-dimensional (3D) probabilitymap, the above process, including the steps S1-S6, may be applied toeach of all MRI slices (including the MRI slice 10) of the subjectarranged in the z direction perpendicular to the x and y directions.

The invention provides a computing method, i.e., the steps S1-S6, toobtain measures of the specific MRI parameters for multiple largeregions or volumes of the MRI image 10 (i.e., the stops of the movingwindow 2), each including multiple voxels of the MRI image 10, andobtain a probability map having small regions (i.e., computation voxels)with extremely accurate probabilities based on the measures of thespecific MRI parameters for the large regions or volumes, whichoverlaps, of the MRI image 10. Because of calculation for theprobabilities based on the large regions or volumes of the MRI image 10,registered or aligned errors between the registered image sets (orregistered parameter maps) can be compensated.

In the algorithm depicted in FIG. 8, some of the steps S11-S16, forexample, may be perfoimed on one or more MRI machines. In the computingmethod depicted in FIG. 4, the steps S1-S6, for example, may beperformed on a MRI system, which may include one or more MRI machines toperform some or all of the steps S11-S16. A probability map foroccurrence of prostate cancer, for example, may be formed by the MRIsystem to perform the steps S1-S6 and shows a probability of cancer fora small portion of the prostate.

By repeating the stops S1-S6 or the steps S5 and S6 for various eventssuch as occurrence of prostate cancer, occurrence of small cell subtype,and occurrence of Gleason scores greater than 7, multiple probabilitymaps for the various events are obtained or formed. The probabilitymaps, for example, include a prostate cancer probability map shown inFIG. 18A, a small cell subtype probability map shown in FIG. 18B, and aprobability map of Gleason scores greater than 7 shown in FIG. 18C. Someor all of the probability maps may be selected to be combined into acomposite probability image or map to provide most useful information tointerpreting Radiologist and Oncologist. The composite probability imageor map may show areas of interest. For example, the compositeprobability image or map shows areas with high probability of cancer(>98%), high probability of small cell subtype, and high probability ofGleason score >7, as shown in FIG. 18D.

In an alternative embodiment, the subset data DB-1 may further includemeasures for a PET parameter (e.g., SUVmax) and a SPECT parameter. Inthis case, the classifier CF, e.g., Bayesian classifier, for the event(e.g., occurrence of prostate cancer) may be created based on dataassociated with the event and specific variables, including, e.g., thePET parameter, the SPECT parameter, some or all of the MRI parametersdepicted in the section of the “description of classifier CF”, and theprocessed parameters of average Ve and average Ktrans, in the subsetdata DB-1. Next, by using the computing method depicted in FIG. 4, theprobability map for the event may be generated or formed based onmeasures of the specific variables for each stop of the moving window 2.

In the invention, the computing method (i.e., the steps S1-S6) depictedin FIG. 4, for example, may be performed on a software, a device, or asystem including, e.g., hardware, one or more computing devices,computers, processors, software, and/or tools to obtain theabove-mentioned probability map(s) for the event(s) and/or theabove-mentioned composite probability image or map. Accordingly, adoctor questions the software, device or system about a suspected regionof an image such as MRI slice image, and the latter provides aprobability map for the event (e.g., occurrence of prostate cancer)and/or a likelihood measurement of cancer (e.g., malignancy) as ananswer.

Second Embodiment:

In the case of the MRI image 10 obtained from the subject (e.g., humanpatient) that has been given a treatment, such as neoadjuvantchemotherapy or (preoperative) radiation therapy, or has taken or beeninjected with one or more drugs for a treatment, such as neoadjuvantchemotherapy, the effect of the treatment or the drugs on the subjectmay be evaluated, identified, or determined by analyzing the probabilitymap(s) for the event(s) depicted in the first embodiment and/or thecomposite probability image or map depicted in the first embodiment.Accordingly, a method of evaluating, identifying, or determining theeffect of the treatment or the drugs on the subject may include thefollowing steps: (a) administering to the subject the treatment or thedrugs, (b) after the step (a), obtaining the MRI image 10 from thesubject by the MRI system, (c) after the step (b), performing the stepsS2-S6 to obtain the probability map(s) for the event(s) depicted in thefirst embodiment and/or obtaining the composite probability image or mapdepicted in the first embodiment, and (d) after the step (c), analyzingthe probability map(s) for the event(s) and/or the composite probabilityimage or map.

Third Embodiment:

The steps S1-S6 may be employed to generate a probability map of breastcancer. In this case, in the steps S1 and S2, the MRI image 10 shows thebreast anatomical structure of the subject as shown in FIG. 19, and thecomputation region 12, set in the desired or anticipated region 11 ofthe MRI image 10, is defined with the computation voxels and covers atleast 10, 25, 50, 80, 90 or 95 percent of the FOV of the MRI image 10,which includes the breast anatomical structure. The steps S3 and S4 arethen sequentially performed. Next, in the step S5, a probability ofbreast cancer for each stop of the moving window 2 may be obtained bymatching the parameter set for said each stop of the moving window 2from the step S4 (or the measures of some or all of the specific MRIparameters for said each stop of the moving window 2 from the step S3)to the classifier CF created for breast cancer. In the step S6, thealgorithm including the steps S11-S16 illustrated in FIG. 8 is performedbased on the probabilities of breast cancer for the stops of the movingwindow 2 to compute probabilities of breast cancer for the respectivecomputation voxels, and the probabilities of breast cancer for therespective computation voxels form the probability map of breast cancer.

Fourth Embodiment:

FIG. 21 is a flow chart of evaluating, identifying, or determining theeffect of a treatment, such as neoadjuvant chemotherapy or(preoperative) radiation therapy, or a drug for the treatment on asubject (e.g., human or animal). Referring to FIG. 21, in a step S21, afirst MRI slice image is obtained from the subject by the MRI device orsystem. The first MRI slice image is composed of multiple voxels in itsfield of view (FOV) to show an anatomical region of the subject, such asprostate or breast. In a step S22, the steps S2-S6 are performed on thefirst MRI slice image to generate a first probability map.

After the step S21 or S22 is performed, step S23 is performed. In thestep S23, the subject is given the treatment, such as a drug givenintravenously or orally. For certain cancers such as prostate cancer,the treatment may be the (preoperative) radiation therapy (or calledradiotherapy), a proton beam therapy, a minimally invasive treatment(such as ablation or radiation), or an ablation therapy such ashigh-intensity focused ultrasound treatment. The (preoperative)radiation therapy for prostate cancer may be performed by a radiotherapydevice such as Truebeam or CyberKnife and may use high-energy radiation(e.g., gamma rays) to shrink tumors and kill cancer cells.

In a step S24, after the subject gets or receives the treatment such asan oral or intravenous drug, a second MRI slice image is obtained fromthe subject by the MRI device or system. The second MRI slice image iscomposed of multiple voxels in its FOV to show the same anatomicalregion of the subject as the first MRI slice image shows. In a step S25,the steps S2-S6 are performed on the second MRI slice image to generatea second probability map. The first and second probability maps may begenerated for an event or data type, such as prostate cancer, breastcancer, one of Gleason scores 0 through 10, two or more of Gleasonscores 0 through 10 (e.g., Gleason scores greater than 7), tissuenecrosis, or the percentage of cancer in a specific range from a firstpercent (e.g., 91 percent) to a second percent (e.g., 100 percent).Next, in a step S26, by comparing the first and second probability maps,the effect of the treatment or the drug used in the treatment on thesubject may be identified, determined, or evaluated as effective orineffective. Based on the result from the step S26, a doctor can decideor judge whether the treatment or the drug should be adjusted orchanged. The method depicted in the steps S21-S26 can detect responsesor progression after the treatment or the drug within less than one weekor two weeks, allowing earlier adjustments to the treatment regime.

Fifth Embodiment:

FIG. 22 is a flow chart of evaluating, identifying, or determining theeffect of a treatment, such as neoadjuvant chemotherapy or(preoperative) radiation therapy, or a drug for the treatment on asubject (e.g., human or animal). Referring to FIG. 22, in a step S31, afirst MRI slice image is obtained from the subject by the MRI device orsystem. The first MRI slice image is composed of multiple voxels in itsfield of view (FOV) to show an anatomical region of the subject, such asprostate or breast. In a step S32, the steps S2-S5 are performed on thefirst MRI slice image to obtain first probabilities of an event or datatype for stops of the moving window 2 for the computation region 12 ofthe first MRI slice image. In other words, the first probabilities ofthe event or data type for the stops of the moving window 2 on the firstMRI slice image for the subject before the treatment are obtained basedon measures of the specific MRI parameters for the stops of the movingwindow 2 on the first MRI slice image to match a matching dataset fromthe established classifier CF or biomarker library. The measures of thespecific MRI parameters for the stops of the moving window 2 on thefirst MM slice image, for example, may be obtained from a registered(multi-parametric) image dataset including, e.g., the first MRI sliceimage and/or different parameter maps registered to the first MRI slice.The event or data type, for example, may be prostate cancer, breastcancer, one of Gleason scores 0 through 10, two or more of Gleasonscores 0 through 10 (e.g., Gleason scores greater than 7), tissuenecrosis, or the percentage of cancer in a specific range from a firstpercent (e.g., 91 percent) to a second percent (e.g., 100 percent).

After the step S31 or S32 is performed, step S33 is performed. In thestep S33, the subject is given the treatment, such as a drug givenintravenously or orally. For certain cancers such as prostate cancer,the treatment may be the (preoperative) radiation therapy (or calledradiotherapy), a proton beam therapy, a minimally invasive treatment(such as ablation or radiation), or an ablation therapy such ashigh-intensity focused ultrasound treatment. The (preoperative)radiation therapy for prostate cancer may be performed by a radiotherapydevice such as Truebeam or CyberKnife and may use high-energy radiation(e.g., gamma rays) to shrink tumors and kill cancer cells.

In a step S34, after the subject gets or receives the treatment such asan oral or intravenous drug, a second MRI slice image is obtained fromthe subject by the MRI device or system. The second MRI slice image iscomposed of multiple voxels in its FOV to show the same anatomicalregion of the subject as the first MRI slice image shows. In a step S35,the steps S2-S5 arc performed on the second MRI slice image to obtainsecond probabilities of the event or data type for stops of the movingwindow 2 for the computation region 12 of the second MRI slice image. Inother words, the second probabilities of the event or data type for thestops of the moving window 2 on the second MRI slice image for thesubject after the treatment are obtained based on measures of thespecific MRI parameters for the stops of the moving window 2 on thesecond MRI slice image to match the matching dataset from theestablished classifier CF or biomarker library. The measures of thespecific MRI parameters for the stops of the moving window 2 on thesecond MRI slice image, for example, may be obtained from a registered(multi-parametric) image dataset including, e.g., the second MRI sliceimage and/or different parameter maps registered to the second MRIslice.

The stops of the moving window 2 for the computation region 12 of thefirst MRI slice may substantially correspond to or may be substantiallyaligned with or registered to the stops of the moving window 2 for thecomputation region 12 of the second MRI slice, respectively. Each of thestops of the moving window 2 for the computation region 12 of the firstMRI slice and the registered or aligned one of the stops of the movingwindow 2 for the computation region 12 of the second MRI slice maysubstantially cover the same anatomical region of the subject.

Next, in a step S36, the first and second probabilities of the event ordata type for each aligned or registered pair of the stops of the movingwindow 2 on the first and second MRI slice images are subtracted fromeach other into a corresponding probability change PMC for said eachaligned or registered pair of the stops of the moving window 2 on thefirst and second MRI slice images. For example, for each aligned orregistered pair of the stops of the moving window 2 on the first andsecond MRI slice images, the probability change PMC may be obtained bysubtracting the first probability of the event or data type from thesecond probability of the event or data type.

In a step S37, the algorithm, including the steps S11-S16, depicted inthe step S6 is performed based on the probability changes PMCs for thealigned or registered pairs of the stops of the moving window 2 on thefirst and second MRI slice images to compute probability changes PVCsfor respective computation voxels used to compose a probability changemap for the event or data type, as described below. Referring to FIG. 8,in the step S11, the probability change PVC for each of the computationvoxels is assumed by, e.g., averaging the probability changes PMCs ofthe aligned or registered pairs, of the stops of the moving window 2 onthe first and second MRI slice images, each having their aligned orregistered squares 6 overlapping or covering said each of thecomputation voxels. In the step S12, a probability guess PG for eachaligned or registered pair of the stops of the moving window 2 on thefirst and second MRI slice images is calculated by, e.g., averaging theprobability changes PVCs for all the computation voxels inside said eachaligned or registered pair of the stops of the moving window 2 on thefirst and second MRI slice images.

In the step S13, a difference DW between the probability guess PG andthe probability change PMC for each aligned or registered pair of thestops of the moving window 2 on the first and second MRI slice images iscalculated by, e.g., subtracting the probability change PMC for saideach aligned or registered pair of the stops of the moving window 2 onthe first and second MRI slice images from the probability guess PG forsaid each aligned or registered pair of the stops of the moving window 2on the first and second MRI slice images. In the step S14, an absolutevalue of the difference DW for each aligned or registered pair of thestops of the moving window 2 on the first and second MRI slice images iscompared with the preset threshold error or value to determine whetheran error, i.e., the absolute value of the difference DW, between theprobability guess PG and the probability change PMC for each aligned orregistered pair of the stops of the moving window 2 on the first andsecond MRI slice images is less than or equal to the preset thresholderror or value. If the absolute values of the differences DWs for allthe aligned or registered pairs of the stops of the moving window 2 onthe first and second MRI slice images are determined in the step S14 tobe less than or equal to the preset threshold error or value, the stepS16 continues. In the step S16, the probability changes PVCs for thecomputation voxels are determined to be optimal, which are calledoptimal probability changes hereinafter, and the optimal probabilitychanges of the computation voxels form the probability change map forthe event or data type. After the optimal probability changes for thecomputation voxels are obtained in the step S16, the algorithm iscompleted.

If any one of the absolute values of the differences DWs for all thealigned or registered pairs of the stops of the moving window 2 on thefirst and second MRI slice images is determined in the step S14 to begreater than the preset threshold error or value, the step S15continues. In the step S15, the probability change PVC for each of thecomputation voxels is updated or adjusted by, e.g., subtracting an errorcorrection factor ECF for said each of the computation voxels from theprobability change PVC for said each of the computation voxels. Theerror correction factor ECF for each of the computation voxels iscalculated by, e.g., summing error correction contributions from thealigned or registered pairs, of the stops of the moving window 2 on thefirst and second MRI slice images, each having their aligned orregistered squares 6 covering or overlapping said each of thecomputation voxels; each of the error correction contributions to saideach of the computation voxels, for example, may be calculated bymultiplying the difference DW for a corresponding one of the aligned orregistered pairs of the stops of the moving window 2 on the first andsecond MRI slice images by an area ratio of an overlapped area betweensaid each of the computation voxels and the corresponding one of thealigned or registered pairs of the stops of the moving window 2 on thefirst and second MRI slice images to a common area of the squares 4inscribed in the corresponding one of the aligned or registered pairs ofthe stops of the moving window 2 on the first and second MR1 sliceimages. After the probability changes PVCs for the computation voxelsare updated, the steps S12-S15 are performed repeatedly based on theupdated probability changes PVCs for the computation voxels in the stepS15, until the absolute values of the differences DWs for all thealigned or registered pairs of the stops of the moving window 2 on thefirst and second MRI slice images are determined in the step S14 to beless than or equal to the preset threshold error or value.

The above process uses the moving window 2 in the x and y directions tocreate a 2D probability change map. In addition, the above process maybe applied to multiple MRI slices of the subject registered in the zdirection, perpendicular to the x and y directions, to form a 3Dprobability change map.

In a step S38, by analyzing the probability change map, the effect ofthe treatment or the drug used in the treatment on the subject may beidentified, determined, or evaluated as effective or ineffective. Basedon the result from the step S38, a doctor can decide or judge whetherthe treatment or the drug should be adjusted or changed. The methoddepicted in the steps S31-S38 can detect responses or progression afterthe treatment or the drugs within less than one week or two weeks,allowing earlier adjustments to the treatment regime.

Sixth Embodiment:

FIG. 23 is a flow chart of evaluating, identifying, or determining theeffect of a treatment, such as neoadjuvant chemotherapy or(preoperative) radiation therapy, or a drug used in the treatment on asubject (e.g., human or animal). Referring to FIG. 23, in a step S41, afirst MRI slice image is obtained from the subject by the MRI device orsystem. The first MRI slice image is composed of multiple voxels in itsfield of view (FOV) to show an anatomical region of the subject, such asprostate or breast. In a step S42, the steps S2-S6 are performed on thefirst MRI slice image to generate a first probability map composed offirst computation voxels.

After the step S41 or S42 is performed, step S43 is performed. In thestep S43, the subject is given a treatment such as an oral orintravenous drug. For certain cancers such as prostate cancer, thetreatment may be the (preoperative) radiation therapy (or calledradiotherapy), a proton beam therapy, or an ablation therapy such ashigh-intensity focused ultrasound treatment. The (preoperative)radiation therapy for prostate cancer may be performed by a radiotherapydevice such as Truebeam or CyberKnife and may use high-energy radiation(e.g., gamma rays) to shrink tumors and kill cancer cells.

In a step S44, after the subject gets or receives the treatment such asan oral or intravenous drug, a second MRI slice image is obtained fromthe subject by the MRI device or system. The second MRI slice image iscomposed of multiple voxels in its FOV to show the same anatomicalregion of the subject as the first MRI slice image shows. In a step S45,the steps S2-S6 are performed on the second MRI slice image to generatea second probability map composed of second computation voxels. Each ofthe second computation voxels may substantially correspond to or may besubstantially aligned with or registered to one of the first computationvoxels. The first and second probability maps may be generated for anevent or data type such as prostate cancer, breast cancer, one ofGleason scores 0 through 10, two or more of Gleason scores 0 through 10(e.g., Gleason scores greater than 7), tissue necrosis, or thepercentage of cancer in a specific range from a first percent (e.g., 91percent) to a second percent (e.g., 100 percent).

In a step S46, by subtracting a probability for each of the firstcomputation voxels from a probability for the corresponding, registeredor aligned one of the second computation voxels, a correspondingprobability change is obtained or calculated. Accordingly, a probabilitychange map is formed or generated based on the probability changes.Next, in a step S47, by analyzing the probability change map, the effectof the treatment or the drug used in the treatment on the subject may beidentified, determined, or evaluated as effective or ineffective. Basedon the result from the step S47, a doctor can decide or judge whetherthe treatment or the drug should be adjusted or changed. The methoddepicted in the steps S41-S47 can detect responses or progression afterthe treatment or the drug within less than one week or two weeks,allowing earlier adjustments to the treatment regime.

The steps, features, benefits and advantages that have been discussedare merely illustrative. None of them, nor the discussions relating tothem, are intended to limit the scope of protection in any way. Numerousother embodiments are also contemplated. These include embodiments thathave fewer, additional, and/or different steps, features, benefits andadvantages. These also include embodiments in which the steps arcarranged and/or ordered differently.

What is claimed is:
 1. A method for detecting a probability of an eventin a tissue sample based on generating a probability map of an image ofthe tissue sample, the method comprising: defining, by the imagingsystem, boundaries of a plurality of computation voxels over at least aportion of the image of the tissue sample, wherein each the plurality ofcomputation voxels is a unit of the probability map; applying, by theimaging system, a moving window to the plurality of computation voxelsin incremental steps; obtaining, by the imaging system, multiplemeasures of multiple imaging parameters for every step of the movingwindow across the image; and determining, by the imaging system, valuesfor each of the plurality of computation voxels, wherein the values foreach of the plurality of computation voxels are based on the multiplemeasures of the multiple imaging parameters; and generating theprobability map using the values for each of the plurality ofcomputation voxels, wherein the probability map provides an indicationof a likelihood of an event at a plurality of locations within thetissue sample.
 2. The method of claim 1, wherein the imaging parameterscomprise at least four types of magnetic resonance imaging (MRI)parameters.
 3. The method of claim 1, wherein two neighboring ones ofthe steps of the moving window are shifted from each other by a distancesubstantially equal to a side length of one of the computation voxels.4. The method of claim 1, wherein the event is that a cancer occurs. 5.The method of claim 1, wherein determining the probability map of theevent comprises: calculating multiple assumed probabilities for therespective computation voxels of the probability map based on the firstprobabilities of the event for the steps of the moving window coveringthe respective computation voxels; calculating multiple probabilityguesses for the respective steps of the moving window based on theassumed probabilities for the computation voxels of the probability mapwithin the respective steps of the moving window; calculating multipledifferences each between one of the probability guesses and one of thefirst probabilities for one of the steps of the moving window; andupdating the assumed probabilities for the respective computation voxelsof the probability map based on the differences for the steps of themoving window covering the respective computation voxels of theprobability map.
 6. A method of forming a probability map, comprising:applying, by an imaging system, a moving window over a first portion ofan image of a tissue at a first stop; obtaining, by the imaging system,multiple first measures of multiple imaging parameters for the firststop of the moving window from the image; moving, by the imaging system,the moving window relative to the image in a direction at a fixedinterval to a second stop; obtaining, by the imaging system, multiplesecond measures of the multiple imaging parameters for the second stop,wherein the first stop of the moving window is partially overlapped withthe second stop of the moving window; obtaining, by the imaging system,a first probability of a first event for the first stop of the movingwindow, wherein the first probability of the first event is based on thefirst measures; obtaining, by the imaging system, a second probabilityof the first event for the second stop of the moving window, wherein thesecond probability of the first event is based on the second measures;and determining, by the imaging system, the probability of a biomarkerat a first location in the tissue based, at least in part, on the firstprobability and the second probability.
 7. The method of claim 6,wherein the image of the tissue comprises a magnetic resonance imaging(MRI) image.
 8. The method of claim 6, wherein the imaging parameterscomprise at least four types of magnetic resonance imaging (MRI)parameters.
 9. The method of claim 6, wherein the first event is that acancer occurs.
 10. The method of claim 6, wherein the first and secondstops of the moving window are shifted from each other by a distancesubstantially equal to a side length of the computation voxel of theprobability map.
 11. The method of claim 6, wherein the dimension of themoving window has a size defined based on volumes of multiple biopsytissues in a subset of tissue samples.
 12. The method of claim 6,wherein the dimension of the moving window has a volume defined based onvolumes of multiple biopsy tissues in a subset of tissue samples. 13.The method of claim 6, wherein the moving window has a circular shapewith a radius defined based on information associated with volumes ofmultiple biopsy tissues in the subset of tissue samples.
 14. The methodof claim 6, wherein the first classifier comprises a Bayesianclassifier.
 15. The method of claim 6, wherein the first classifier iscreated based on information associated with multiple third measures ofthe imaging parameters for multiple biopsy tissues in a subset of tissuesamples and multiple diagnoses for the multiple biopsy tissues in thesubset of tissue samples.
 16. The method of claim 6 further comprisingcalculating a third probability of the first event for the at least oneof the computation voxels of the probability map based on the first andsecond probabilities of the first event, wherein said first and secondstops of the moving window overlap the at least one of the computationvoxels of the probability map.
 17. The method of claim 6 furthercomprising calculating a third probability of the first event for one ofmultiple computation voxels of the probability map, wherein calculatingthe third probability of the first event comprises: assuming the thirdprobability of the first event by averaging the first and secondprobabilities; calculating a first probability guess for the first stopof the moving window by averaging a first group of probabilities of thefirst event for a first group of the plurality of computation voxelsinside the first stop of the moving window, wherein the first group ofprobabilities of the first event comprise the third probability of thefirst event; calculating a second probability guess for the second stopof the moving window by averaging a second group of probabilities of thefirst event for a second group of the plurality of computation voxelsinside the second stop of the moving window, wherein the second group ofprobabilities of the first event comprise the third probability of thefirst event; and determining whether a first absolute value of a firstdifference between the first probability guess and the first probabilityof the first event and a second absolute value of a second differencebetween the second probability guess and the second probability of thefirst event are less than or equal to a preset threshold value.
 18. Themethod of claim 17, wherein calculating the third probability of thefirst event further comprises updating the third probability of thefirst event based on information associated with the first and seconddifferences.
 19. The method of claim 6 further comprising: obtaining athird probability of a second event for the first stop of the movingwindow by matching the first measures to a second classifier; obtaininga fourth probability of the second event for the second stop of themoving window by matching the second measures to the second classifier;calculating a fifth probability of the first event based on the firstand second probabilities of the first event; calculating a sixthprobability of the second event based on the third and fourthprobabilities of the second event; and creating a composite probabilitymap based on information associated with the fifth probability of thefirst event and the sixth probability of the second event.
 20. Themethod of claim 19, wherein the first event is that a cancer occurs, andthe second event is associated with a Gleason score.